Machine Learning Jobs in Dubai

Join one of the world's leading multi-strategy investment platforms as a Quant Researcher focused on Machine Learning and AI. This role sits within a highly successful systematic investment team that is investing heavily in next-generation research capabilities and the application of cutting-edge machine learning techniques to financial markets.The team is seeking exceptional researchers who can bring expertise from frontier AI, deep learning, large-scale modelling, and modern machine learning research into a fast-paced environment where ideas are rapidly tested and deployed. This is an opportunity to work on some of the most challenging prediction problems in the world while having direct impact on investment performance.ResponsibilitiesDevelop and implement machine learning models to identify predictive signals across global marketsResearch and apply state-of-the-art deep learning techniques for alpha generationDesign and test neural network architectures for financial forecasting and pattern recognitionWork with large, complex, and alternative datasets to uncover unique sources of investment insightConduct rigorous research, experimentation, and statistical validation of trading signalsCollaborate closely with Portfolio Managers, Quant Researchers, and Data ScientistsBuild scalable research infrastructure and production-ready modelling pipelinesStay at the forefront of developments in AI, machine learning, and quantitative researchContribute to the development of systematic investment strategies using modern ML techniquesRequirementsPhD or Master's degree in Machine Learning, Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative disciplineStrong background in machine learning research and modellingExperience building and deploying deep learning models in real-world environmentsExpertise in areas such as neural networks, representation learning, transformers, foundation models, reinforcement learning, probabilistic modelling, or large-scale optimisationExcellent programming skills in Python and familiarity with modern ML frameworks such as PyTorch or TensorFlowStrong mathematical and statistical foundationsDemonstrated ability to conduct independent research and solve complex problemsExperience from leading AI labs, deep technology companies, research institutions, or high-performance machine learning environments is highly desirablePreferred BackgroundsWe are particularly interested in candidates from:Leading AI research labsFrontier foundation model teamsDeep learning and applied AI organisationsResearch-focused technology companiesQuantitative research groupsHigh-performance computing and data science environmentsAcademic researchers with strong publication records in machine learning or artificial intelligenceWhat You'll GetOpportunity to apply cutting-edge AI research to real-world investment decisionsAccess to significant computational, data, and research resourcesHighly collaborative environment alongside world-class investors and researchersCompetitive compensation package with substantial upside potentialDirect impact on investment outcomes and business growthRelocation support to Dubai where applicableExposure to one of the most intellectually challenging applications of machine learningThis position is ideal for researchers who are passionate about solving difficult prediction problems, building innovative machine learning systems, and applying frontier AI techniques in a highly competitive and rewarding environment. Desired Skills and ExperienceSeeking elite ML Researchers and PhDs to develop deep learning models for alpha generation within a highly capitalised, technology-driven investment team.

ABOUT XANTORYXantory runs a vertical-farming control platform that plans, drives and records every crop cycle: climate, light, irrigation and dosing across rooms and racks, from a cloud planning tier down to controllers on the racks. Every reading, every relay switch, every recorded stage boundary and every harvest is stored. What the platform cannot do yet is see the plants.As the first machine-learning engineer on the team, the Machine Learning Engineer – Computer Vision will build that capability: cameras on the racks, models that read the crop, and the pipeline that turns what they see into alerts, records and control decisions. The role will also put the sensor and outcome data already collected to work, and will be measured on what runs in production in front of real crops.Role OverviewYou will own the work end to end. That starts with specifying the imaging setup and building our first labelled datasets on site, then training models for plant counting, growth stages, stress and disease, and harvest readiness, and deploying them at the edge and on the site server. You will also combine vision results with the sensor and yield data we already collect to compare growing recipes on evidence. This is a hands-on, production-focused role based in Dubai with regular on-site farm work. Success is measured by what runs reliably in front of real crops.Key Responsibilities1. Computer Vision (Core of the Role)Specify the imaging setup for racks and trays: camera selection and placement, lighting under grow-light spectra, capture schedule, and the image and annotation standards that make a dataset trustworthy.Build the farm's first labelled image datasets from our own crops, starting with data collection on site.Train and deploy models for plant detection and counting, germination and growth-stage recognition, canopy coverage, stress and disease indication, and harvest readiness.Choose the right approach per problem (classification, detection, segmentation, anomaly detection) and prove it with evaluation that holds up on new crops and new racks.Deploy inference at the edge (Raspberry Pi-class devices today, Jetson-class where justified) and on the site server, balancing accuracy, latency and hardware limits.2. Sensor & Outcome DataCombine what the cameras see with time-series data (temperature, humidity, CO₂, PPFD, pH, EC, flow, actuator history, recorded stage boundaries and yields) to explain outcomes against the recipe.Model cycle length and yield per rack against growing recipes and compare recipes based on evidence.3. Integration & OperationShip models as services that fit the platform, including Rust, PostgreSQL and Redpanda for data, Kubernetes for deployment, and results surfaced in Sentinel.Version datasets, experiments and models; monitor model performance and degradation; retrain on production feedback.Define the data contracts your models consume with backend, edge and console engineers.Qualifications & Experience4 years of hands-on computer vision experience with PyTorch or TensorFlow, including at least one vision system taken from your own data collection and kept running.Experience building image datasets from scratch, including quality control, class balance, and the honest handling of small and shifting datasets.Practical experience with cameras and video pipelines, including exposure, colour under artificial light, and calibration.Exposure to horticulture, controlled-environment agriculture (CEA), plant phenotyping or industrial quality inspection is an advantage.Experience with multispectral or NIR imaging is an advantage.Skills & CompetenciesSolid Python and ML fundamentals, including leakage-safe validation.Comfortable in a production engineering environment: Git, code review, tests, containers, and working against services others own.Edge inference experience (ONNX, TensorRT, NVIDIA Jetson, Raspberry Pi deployments) is an advantage.Familiarity with MLOps tooling (experiment tracking, model registry and monitoring) is an advantage.Knowledge of Rust or Go, MQTT and Kubernetes is an advantage.Clear written and spoken English; able to explain a model's limits to a grower and its interface to a backend engineer.How Success Will Be MeasuredProduction deployment: Number of vision models running in production on live racks, and time from data collection to first deployment.Model accuracy: Detection, counting and growth-stage accuracy validated on new crops and new racks, not only the training set.Edge performance: Inference latency, uptime and resource use on Raspberry Pi / Jetson-class devices and the site server.Dataset quality: Coverage of labelled datasets across crops and growth stages, annotation consistency and class balance.Model health: Detection of performance degradation and time to retrain on production feedback.Operational impact: Alerts and records adopted by growers, and yield / cycle-length insights used in recipe decisions.Work Location: In person

Job title: Machine Learning Engineer 1Location: Dubai, UAEAbout noonWe’re building an ecosystem of digital products and services that power everyday life across the Middle East—fast, scalable, and deeply customer-centric. Our mission is to deliver to every door every day. We want to redefine what technology can do in this region, and we’re looking for a (add title) who can help us move even faster.noon’s mission: Every door, every day.What you'll do:Team noon has some of the fastest, smartest, and hardest-working people we've encountered. As a Machine Learning Engineer (MLE1), Ads, you will be a core individual contributor responsible for the hands-on design, development, and deployment of production-grade Machine Learning models across our Ads division. You will be responsible for delivering high-impact models and deployments that power critical functions like ranking, bidding, and click-fraud detection.This role is deeply technical; you will partner with Product and Engineering teams across noon Ads to turn complex data into competitive advantagesDeeply engage in the technical work: building, training, and deploying production-grade models across our domainsContribute to solving "hard" problems involving relevance, ranking, bidding optimization, and fraud detectionPartner with the broader engineering team to implement optimal deployment strategies for heavy ML workloadsAdhere to and contribute to the standardization of ML Ops and best practices within the teamWhat you'll need:Experience: hands-on experience or relevant internships in Machine Learning or data science is preferredFoundational Knowledge: Strong academic or practical grounding in core machine learning concepts, statistics, and data structuresCoding & Frameworks: Proficiency in Python and familiarity with standard ML libraries (e.g., PyTorch, TensorFlow, Scikit-learn) and SQLGrowth & Collaboration: Eagerness to learn from senior engineers, collaborate across product and engineering teams, and rapidly ramp up on MLOps best practicesTech Stack: Basic exposure to cloud platforms (like GCP) and data processing pipelines.

AI / ML EngineerDubai About the Company: Our client is an AI and robotics startup building autonomous systems for heavy duty environments. Based in the UAE, they design, build and deploy intelligent autonomy end-to-end, helping enterprise clients bring automation into complex, high risk operations.The RoleYou will own how the vehicles see and understand the world. That means building the perception layer that turns raw sensor data into a reliable picture of the environment in real conditions: dust, glare, heat, uneven terrain and places no one has mapped. Your work feeds directly into navigation, obstacle avoidance and fleet management. This is a hands-on role with high ownership, and you will be expected to get code running on real vehicles early.What You Will DoDesign, build and deploy the perception stack across camera, LiDAR and radar inputsDevelop object detection, tracking and segmentation that holds up in harsh outdoor and industrial settingsBuild localisation and mapping capability, including operation in GPS-denied and unmapped areasFuse multi-sensor data into a unified, real-time environmental model the planning stack can trustOptimise models for on-vehicle inference on edge hardware within strict latency and compute budgetsBuild data pipelines for collection, labelling, validation and retraining from fleet dataFeed perception insights into fleet-level analytics for mission planning and performance monitoringWork closely with autonomy, controls and hardware engineers to take features from test track to deploymentWhat You Bring3+ years building perception systems that have run on real robots or vehicles, not only curated datasetsStrong computer vision skills: detection, tracking and segmentation on live sensor feedsHands-on experience with SLAM, point cloud processing and multi-sensor calibrationExperience with sensor fusion (e.g. Kalman filtering, factor graphs or learned fusion approaches)Strong Python and C++, Experience deploying models to edge or embedded GPU hardwareComfort with ROS/ROS 2 and field testingOwnership mindsetNice to HaveModel compression and optimisation (quantisation, pruning, TensorRT or similar)Experience with fleet data infrastructure and MLOps for continuous model improvementExposure to simulation environments for perception testingWhy ApplyBuild technology that runs on real vehicles in real missionsEarly-stage ownership in a fast-growing startup

Role: Machine Learning EngineerLocation: Dubai Department: Technology & OperationsReports To: CTOPosition: Full-TimeAbout us:GlobalDrum is empowering the media sector to own, understand, and monetise their most valuable social audiences, using live audience behavioral data for brands to operate diversified business models on demand for themselves. We are evolving our unique next-generation B2B2C Platform as a Service (PaaS) that operates on a globally distributed cloud infrastructure incorporating a scalable, event-driven architecture using the latest AI and data techniques to evolve an entire sector.Located in London, Dubai, New York and Dubai, the Company is at the centre of reshaping how global brands will evolve in a sector valued at $276Bn in 2025 representing 30% of all digital ad spending. If this seems too ambitious for you, then don't apply. Job Description:We are looking for a visionary Machine Learning Engineer to become our first-ever ML hire, embedding data science and applied ML into the heart of our AI-native, multi-tenant platform. You will work directly with the Director of Engineering and product owners to turn audience, content, and revenue data into models that power recommendations, audience understanding, and monetisation decisions across the platform.This is a rare opportunity to define the ML function from the ground up: choosing the stack, setting the standards, and building the first production models that the rest of the engineering org will be built around.Key Responsibilities:Own Recommendations End-to-End: Design, build, and iterate on recommender systems that surface content and audience opportunities to our media partners.Build the ML Foundation: Establish the first ML pipelines, feature stores, experimentation frameworks, and model-serving infrastructure for the platform, since none currently exist.Data-to-Product Pipeline: Partner with the engineering team to design real-time, event-driven data pipelines that feed model training and low-latency inference.Experimentation & Evaluation: Define offline and online evaluation methodology (A/B testing, holdout sets, ranking metrics) to validate model impact on engagement and revenue.Set ML Standards: Establish best practices for model development, versioning, reproducibility, monitoring, and responsible use of data Stay Hands-On: Write production-grade ML code, heavily leveraging modern AI coding assistants and agentic development workflows to move fast as a team of one (initially).Cross-Functional Partnership: Work closely with the Engineering and platform/revenue product owners to translate business and product questions into ML-solvable problems.Scale the Function: As the first ML hire, help define the roadmap and hiring plan for the ML team as it grows.Required Skills and Qualifications:Proven, hands-on experience building and deploying recommender systems in production Working knowledge of graph-based learning, including Graph Neural Networks.Solid understanding of network science fundamentals.Experience designing and training models on large-scale, real-world data, including handling sparsity, and skewed engagement distributions.Comfortable owning the full ML lifecycle: feature engineering, training, evaluation, deployment, and monitoringFluency with modern ML tooling (e.g., PyTorch/TensorFlow and standard MLOps practices).Experience using AI coding assistants and agentic workflows to accelerate development.Track record of working effectively in an early-stage startup environment.Bonus Points If You Have...Exposure to the media and/or news technology ecosystem.Experience in AdTech: advertising networks, ad serving, audience segmentation.Preferred Experience/Qualifications:Strong domain knowledge in SaaS/PaaS, MarTech, or AdTech is highly valued.Experience building models that directly optimize platform revenue outcomes.Experience building recommendation systems in production using LLMs and VLMsComfortable being the sole ML voice in the room initially, while communicating clearly with engineering and product stakeholders who may not have deep ML backgroundWhy Join Us?The chance to build a ground-breaking AI platform from day oneCompetitive equity options package and salaryFlexible working environment

Quantitative Researcher (Machine Learning) | Equities, Dubai, UAEAre you completing a PhD and applying cutting-edge Deep Learning techniques to complex real-world problems?A leading quant team based in Dubai is seeking a Quantitative Researcher to join its growing Equities platform. This opportunity is ideal for PhD graduates with expertise in Machine Learning, Deep Learning, Neural Networks, Reinforcement Learning, or Large-Scale Data Science who are interested in applying research to live systematic trading strategies.You will work alongside experienced Quantitative Researchers, Traders, and Technologists to develop next-generation predictive models across global equity markets.What You'll Be Doing- Research and develop alpha signals using Machine Learning and Deep Learning techniques.- Apply Neural Networks, Transformers, Time Series Models, Reinforcement Learning, and other advanced statistical methods to large-scale financial datasets.- Design and improve predictive models for equity forecasting and systematic investing.- Analyse alternative and traditional datasets to identify new sources of alpha.- Collaborate with portfolio managers and engineers to deploy research into production.- Conduct rigorous back-testing and performance analysis.What We're Looking For- PhD (or soon-to-be completed PhD) in Machine Learning, Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative discipline.- Strong research background involving Deep Learning, Neural Networks, Statistical Learning, AI, or Large-Scale Modelling.- Excellent programming skills in Python.- Experience working with large datasets and developing predictive models.- Publications across top ML conferences (NeurIPS, ICML, ICLR, AISTATS, IEEE and etc) academic research, or industry projects involving advanced Machine Learning techniques are highly desirable.

We help the world run betterAt SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.What You’ll BuildIn this role, you'll build and operationalize machine learning and AI capabilities that from idea to production and deliver measurable product value. You'll develop data move pipelines, training and inference workflows, model-serving services, APIs, and evaluation frameworks for use cases such as recommendation, forecasting, anomaly detection, classification, NLP, semantic search, and generative AI.Your work will include feature engineering, experiment design, model tuning, offline and online validation, and integration of models into scalable product architectures. You'll help improve LLM-based workflows, including prompt design, retrieval-augmented generation, vector search, guardrails, and response quality evaluation. You’ll also strengthen the engineering backbone around AI through CI/CD, monitoring, observability, testing, and model lifecycle automation so solutions are reliable, cost-aware, secure, and ready for enterprise scale.What You BringYou bring strong programming skills in Python, along with working knowledge of Java or Go, for building production-grade services and APIsYou have a solid understanding of machine learning fundamentals, including supervised and unsupervised methods such as classification, regression, clustering, ranking, and recommendation systemsYou have hands-on experience with deep learning frameworks (e.g., PyTorch or TensorFlow) for model training, fine-tuning, and inferenceYou demonstrate strong capabilities in data preparation, feature engineering, data validation, and model evaluation using appropriate offline and online metricsYou have experience building, deploying, and integrating ML models into production systems through batch, real-time, or streaming pipelinesYou are familiar with generative AI concepts, including LLMs, embeddings, vector databases, prompt engineering, and retrieval-augmented generation, and how to apply them in practical use casesYou bring working knowledge of MLOps and modern data infrastructure, including experiment tracking, model versioning, CI/CD, and tools such as Spark, Kafka, Airflow, and feature storesYou have experience operating ML systems in production, including monitoring for drift, latency, accuracy, cost, bias, and performing debugging and failure analysis to ensure reliability and business impactAbout YouYou have 1-3+ years of experience in machine learning engineering, software engineering, or a related field, with a track record of deploying models into productionYou are passionate about building reliable, scalable ML systems and take ownership of delivering end-to-end solutionsYou balance experimentation with engineering rigor, making thoughtful trade-offs to ensure models are both innovative and production-readyYou are a collaborative problem-solver who thrives in ambiguous environments and is motivated by delivering measurable business impactWhere You BelongYou will be part of a growing team of AI and industry experts dedicated to serving customers across the Kingdom of Saudi Arabia / United Arab Emirates. We are building a collaborative, high‑impact environment that brings together deep AI expertise, strong industry knowledge, and regional understanding to help customers innovate, transform, and lead in their markets.This team thrives on working together to turn complex business challenges into practical, scalable solutions. You will find an environment that values curiosity, ownership, and continuous learning, where new ideas are encouraged and real‑world impact matters. This is a place for people who enjoy building something new. You will thrive here if you are customer‑centric, motivated by meaningful outcomes, and comfortable navigating ambiguity in a fast‑evolving AI landscape.About The TeamWe are a team of engineers passionate about building valuable, scalable AI frameworks that drive real business impact. We thrive on solving challenging problems, innovating with cutting-edge technologies, and collaborating closely to deliver AI systems that are reliable, high-performing, and ready for production. Our team values ownership, technical excellence, and creating solutions that stand the test of scale and complexity.Bring out your bestSAP innovations help more than four hundred thousand customers worldwide work together more efficiently and use business insight more effectively. Originally known for leadership in enterprise resource planning (ERP) software, SAP has evolved to become a market leader in end-to-end business application software and related services for database, analytics, intelligent technologies, and experience management. As a cloud company with two hundred million users and more than one hundred thousand employees worldwide, we are purpose-driven and future-focused, with a highly collaborative team ethic and commitment to personal development. Whether connecting global industries, people, or platforms, we help ensure every challenge gets the solution it deserves. At SAP, you can bring out your best.We win with inclusionSAP’s culture of inclusion, focus on health and well-being, and flexible working models help ensure that everyone – regardless of background – feels included and can run at their best. At SAP, we believe we are made stronger by the unique capabilities and qualities that each person brings to our company, and we invest in our employees to inspire confidence and help everyone realize their full potential. We ultimately believe in unleashing all talent and creating a better world.SAP is committed to the values of Equal Employment Opportunity and provides accessibility accommodations to applicants with physical and/or mental disabilities. If you are interested in applying for employment with SAP and are in need of accommodation or special assistance to navigate our website or to complete your application, please send an e-mail with your request to Recruiting Operations Team: Careers@sap.com.For SAP employees: Only permanent roles are eligible for the SAP Employee Referral Program, according to the eligibility rules set in the SAP Referral Policy. Specific conditions may apply for roles in Vocational Training.Qualified applicants will receive consideration for employment without regard to their age, race, religion, national origin, ethnicity, gender (including pregnancy, childbirth, et al), sexual orientation, gender identity or expression, protected veteran status, or disability, in compliance with applicable federal, state, and local legal requirements.Successful candidates might be required to undergo a background verification with an external vendor.AI Usage in the Recruitment ProcessFor information on the responsible use of AI in our recruitment process, please refer to our Guidelines for Ethical Usage of AI in the Recruiting Process.Please note that any violation of these guidelines may result in disqualification from the hiring process.Requisition ID: 450237 | Work Area: Software-Design and Development | Expected Travel: 0 - 10% | Career Status: Professional | Employment Type: Regular Full Time | Additional Locations:

Since launching in Kuwait in 2004, talabat, the leading on-demand food and Q-commerce app for everyday deliveries, has been offering convenience and reliability to its customers. talabat’s local roots run deep, offering a real understanding of the needs of the communities we serve in eight countries across the region.We harness innovative technology and knowledge to simplify everyday life for our customers, optimize operations for our restaurants and local shops, and provide our riders with reliable earning opportunities daily.Here at talabat, we are building a high performance culture through engaged workforce and growing talent density. We're all about keeping it real and making a difference. Our 6,000+ strong talabaty are on an awesome mission to spread positive vibes. We are proud to be a multi great place to work award winner.Job DescriptionAs the leading delivery company in the region, we have a great responsibility and opportunity to impact the lives of millions of customers, restaurant partners, and riders. To realize our potential, we need to advance our platform to become much more intelligent in how it understands and serves our users.As a Sr. Data Scientist (AI & ML) on the global AI hub, your mission will be to design, build, and ship the machine learning and generative AI systems that power decisions across product and business. You will own a particular domain end to end, working closely with product and business managers as part of a talented team of data scientists and machine learning engineers. You will own the full ML lifecycle, from problem framing, data modeling, and feature engineering through model training, deployment, serving, and monitoring in production. Many of our initiatives will focus on leveraging Generative AI and LLMs for tasks such as data enrichment, smart content understanding, and automated decision-making to enhance user experiences and business operations at scale.ResponsibilitiesFraming ambiguous business problems as well-defined machine learning and data science problems, with clear, objective success criteria.Providing high-quality, impactful insights and data-driven recommendations through rigorous analysis and automated reporting to drive strategic organizational choices.Designing, building, and shipping end-to-end machine learning and generative AI systems in production — spanning data pipelines, feature engineering, model training, serving, and monitoring.Taking on engineering-heavy work end to end: architecting robust ML-based systems, writing clean and scalable production code, and training, deploying, and maintaining reliable ML models that solve real business problems at scale.Training, evaluating, and iterating on models — selecting the simplest, most appropriate algorithms and architectures to deliver measurable business value.Leveraging LLMs and generative AI for data enrichment, smart content understanding, and automated decision-making within production systems.Building and maintaining the data models, features, and pipelines that power model training and allow us to measure performance and its drivers for your area of focus.Designing, planning, and analyzing experiments (A/B and multivariate tests) to rigorously measure model and product impact.Developing deep familiarity with source data and its generating systems through documentation, collaboration with engineering teams, and systematic data profiling.Partnering with product and business teams to identify high-impact opportunities and translate them into ML solutions and actionable, data-driven recommendations.Mentoring other data scientists in their growth journeys.Elevating engineering and ML best practices — improving our ways of working, tooling, MLOps, and internal training programs.QualificationsTechnical ExperienceDeep expertise in machine learning, generative AI, deep learning, recommendation systems, NLP, pattern recognition, data mining.Deep hands-on knowledge of ML and GenAI frameworks (e.g. Scikit-learn, XGBoost, LightGBM, CatBoost, SVMs, Keras, TensorFlow, PyTorch, Transformers, LLM fine-tuning).Strong software engineering fundamentals: excellent coding skills, a solid grasp of data structures and algorithms, and proven ability in both general system design and ML system design.Proven experience building, deploying, serving, and monitoring ML models in production, with a strong grasp of MLOps practices.Strong data and ML engineering skills, including building and orchestrating data and training pipelines (e.g. via Airflow) and robust feature engineering.Excellent SQL and competence with reproducible analysis and modeling in Python.Solid statistical foundations, including experiment design and analysis (A/B and multivariate) and inferential, causal, and predictive methods.Familiarity with data modeling and dimensional design.Strong command over the entire ML lifecycle, from problem formulation and data auditing through modeling, deployment, interpretation, and presentation.Familiarity with product data (impressions, events, etc.) and product health measurement (conversion, engagement, retention, etc.).Experience with LLMs and NLP-based solutions for data enrichment and smart automation is a plus.Familiarity with BigQuery and the Google Cloud Platform is a plus.QualificationsBachelor's degree in engineering, computer science, technology, or similar fields. A postgraduate degree is a plus but not required.5+ years of experience across data science, machine learning engineering, and generative AI, including shipping ML models to production.Experience building ML systems in an online consumer product setting is a plus.A good problem solver with a 'figure it out' growth mindset.An excellent collaborator.An excellent communicator.A strong sense of ownership and accountability.A 'keep it simple' approach to #makeithappen.

Quantitative Researcher (Machine Learning) | Equities, Dubai, UAEAre you completing a PhD and applying cutting-edge Deep Learning techniques to complex real-world problems?A leading quant team based in Dubai is seeking a Quantitative Researcher to join its growing Equities platform. This opportunity is ideal for PhD graduates with expertise in Machine Learning, Deep Learning, Neural Networks, Reinforcement Learning, or Large-Scale Data Science who are interested in applying research to live systematic trading strategies.You will work alongside experienced Quantitative Researchers, Traders, and Technologists to develop next-generation predictive models across global equity markets.What You'll Be Doing- Research and develop alpha signals using Machine Learning and Deep Learning techniques.- Apply Neural Networks, Transformers, Time Series Models, Reinforcement Learning, and other advanced statistical methods to large-scale financial datasets.- Design and improve predictive models for equity forecasting and systematic investing.- Analyse alternative and traditional datasets to identify new sources of alpha.- Collaborate with portfolio managers and engineers to deploy research into production.- Conduct rigorous back-testing and performance analysis.What We're Looking For- PhD (or soon-to-be completed PhD) in Machine Learning, Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative discipline.- Strong research background involving Deep Learning, Neural Networks, Statistical Learning, AI, or Large-Scale Modelling.- Excellent programming skills in Python.- Experience working with large datasets and developing predictive models.- Publications across top ML conferences (NeurIPS, ICML, ICLR, AISTATS, IEEE and etc) academic research, or industry projects involving advanced Machine Learning techniques are highly desirable.

Welcome to MultiBank Group, a global financial pioneer established in 2005 in California and now proudly headquartered in Dubai, UAE. We specialize in delivering cutting-edge trading technology, unparalleled liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs.Join our thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$ 35 billion. As a heavily regulated institution with oversight from 18+ financial regulators across 5 continents, and recipient of over 80 financial awards, MultiBank Group is devoted to innovation, excellence, and empowering our clients to achieve their financial goals.Role OverviewWe are seeking a Senior Machine Learning Engineer to join our AI team as a technical owner of ML products and infrastructure. This is a deeply hands-on engineering position for someone who builds and scales production AI systems used by real users in real-time environments. The right candidate operates across the full ML lifecycle — from model design through deployment, optimization, and ongoing performance in production — and contributes to the technical direction of the AI platform.Key ResponsibilitiesArchitect and implement robust ML systems in production environments, ensuring scalability, reliability, and performance from day oneBuild and deploy supervised, unsupervised, deep learning, and generative AI models into live production environments at scaleOwn technical design for ML pipelines, feature stores, training infrastructure, and inference systems, driving decisions that balance performance, cost, and maintainabilityDesign and deliver RAG systems, fine-tuning pipelines, prompt engineering frameworks, and evaluation pipelines for production-grade LLM applicationsImplement and maintain CI/CD for ML, model versioning, monitoring, drift detection, and automated retraining pipelinesContinuously optimize model performance, inference latency, cost efficiency, and reliability across live systemsCollaborate with product managers, engineers, and data teams to translate business problems into scalable, maintainable AI solutionsMentor junior and mid-level ML engineers, establish best practices, and contribute to technical standards across the teamContribute to strategic decisions around data architecture, AI infrastructure, and cloud platform directionWork with mobile attribution and customer engagement data sources including Adjust, MoEngage, and Firebase for ML use cases such as churn prediction, personalization, and campaign optimizationRequirements7 to 15 or more years of experience in software engineering, data science, or ML engineeringStrong background in product companies, scale-ups, or enterprise AI platformsProven track record of building production-grade AI systems, not solely notebooks or proof-of-concept workComfortable owning systems end-to-end from data through model through deployment through monitoringProduct-first engineering approach, not research-only profilesAdvanced Python engineering skills with strong systems thinking and a focus on production qualityComfortable with fast iteration cycles and deploying models into live environmentsAbility to work directly and confidently with stakeholders and product ownersFintech or financial services experience is an advantageTechnical SkillsMachine Learning and AI: PyTorch, TensorFlow, XGBoost, LightGBM, Hugging Face (Transformers, Datasets, Diffusers)LLM and GenAI: OpenAI and Anthropic APIs, LangChain, LlamaIndex; RAG architectures with vector DB and retrieval pipelines; embedding models (OpenAI, Cohere, open-source); Pinecone, Weaviate, Milvus, FAISS; fine-tuning via LoRA and PEFT frameworks; evaluation using RAGAS and custom pipelinesMLOps and Production: Docker, Kubernetes, MLflow, Weights and Biases, Airflow, Dagster, Prefect, GitHub Actions, GitLab CI, Evidently AI, Arize, custom observability stacksCloud: AWS (SageMaker, EKS, S3, Lambda), Azure ML, Azure Databricks, GCPData Stack: Databricks, Spark, PySpark, Delta Lake, Apache Iceberg, Lakehouse architecturesWhy Join Us?Work with one of the world’s leading financial derivatives institutions. Competitive salary plus performance-based incentives. Access to a dynamic, international, and fast-growing environment. Strong opportunities for career progression within a global financial group. Be part of a business committed to innovation, excellence, and long-term growth. Become part of our international community at MultiBank Group, dedicated to excellence, innovation, and shaping the future of finance.MultiBank Group is an equal opportunity employer. We welcome applications from candidates of all backgrounds and do not discriminate on the basis of nationality, gender, age, religion, or disability.

Hyderabad, India / Dubai, UAEFull-timeApply to JobAbout The RoleWe are looking for a highly motivated Machine Learning Engineer / Data Scientist to join our dynamic team. In this mid-level role, you will leverage your expertise in data science and machine learning to design, build, and deploy innovative models and solutions that address real-world challenges.You will collaborate with cross-functional teams, including data engineers, product managers, and software developers, to turn complex datasets into actionable insights and build machine learning solutions that drive business value. This is a fantastic opportunity to work on impactful projects, expand your skill set, and contribute to cutting-edge advancements in AI and data science.Key ResponsibilitiesModel Development: Design, train, and evaluate machine learning models to solve business problems such as classification, regression, clustering, and recommendation.Data Preparation: Work with large and complex datasets, performing data cleaning, preprocessing, and feature engineering to optimize model performance.Model Deployment: Deploy machine learning models into production environments, ensuring scalability and robustness.Algorithm Selection: Research and implement state-of-the-art algorithms and methodologies to enhance model accuracy and efficiency.Collaboration: Collaborate with data engineering teams to build data pipelines and ensure efficient data flow for machine learning workflows.Visualization and Reporting: Create clear and actionable visualizations and reports to communicate findings and results to stakeholders.Monitoring and Maintenance: Monitor model performance in production, address drift or bias issues, and optimize models as needed.Tool Development: Build tools and frameworks to enable rapid experimentation and iteration of machine learning models.Documentation: Maintain comprehensive documentation for models, experiments, and processes.QualificationsEducation:Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field (or equivalent experience).Technical SkillsStrong programming skills in Python, R, or similar languages.Experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras.Proficiency in data manipulation and analysis using tools like Pandas, NumPy, and SQL.Experience with big data technologies such as Spark, Hadoop, or similar.Knowledge of cloud platforms and services (e.g., AWS SageMaker, Google AI Platform, Azure ML).Familiarity with MLOps practices and tools for CI/CD in machine learning workflows.Understanding of data visualization tools like Matplotlib, Seaborn, or Tableau.Strong grasp of statistical methods, probability, and optimization techniques.Experience3–5 years of experience in machine learning, data science, or a related field.Proven experience building and deploying machine learning models in production.Experience with natural language processing (NLP), computer vision, or time-series analysis is a plus.Soft SkillsStrong problem-solving and analytical thinking abilities.Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.Ability to work independently and collaboratively within a team.Curiosity and eagerness to stay updated on the latest advancements in machine learning and AI.About The CompanyDatamaze is a dynamic company specializing in AI, Data, and Analytics consulting services. We are dedicated to transforming businesses by leveraging the power of data and artificial intelligence. With a team of industry experts in technology, business strategy, and data science, Datamaze offers a range of services including data strategy development, AI model creation, advanced analytics, and data visualization. Our approach is client-centric, focusing on creating customized solutions that address unique business challenges and objectives. The team, comprising data scientists, AI experts, strategists, and engineers, is committed to continuous learning and innovation.Datamaze positions itself as a trusted partner for businesses looking to navigate the complex data and AI landscape, aiming to optimize operations, inform decision-making, and maintain a competitive edge.Apply Now

We help the world run betterAt SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.What You’ll BuildIn this role, you'll lead the design and delivery of production-grade ML and generative AI systems that solve complex product and business problems at scale. You'll own architecture and implementation across data pipelines, feature stores, training workflows, model-serving infrastructure, online experimentation, and observability.You'll drive advanced use cases across deep learning, NLP, ranking, recommendation, forecasting, semantic retrieval, and LLM applications, including RAG, tool use, evaluation harnesses, and safety controls. Your work will directly shape performance, reliability, latency, and cost efficiency in live environments. You’ll guide technical direction on topics such as model selection, distributed training and inference, GPU utilization, model compression, prompt and retrieval optimization, drift detection, retraining strategy, and responsible AI controls. You’ll mentor other engineers and turn best practices into reusable patterns and platform capabilities.What You BringYou bring expert-level programming skills in Python, along with strong software engineering expertise in languages such as Java or Go, enabling you to build scalable, production-grade systems You have deep knowledge of machine learning, deep learning, and optimization techniques across structured data, NLP, search, ranking, and recommendation problems You have extensive experience designing and operating end-to-end ML systems, from data ingestion and experimentation to deployment, observability, and lifecycle management You bring strong hands-on experience with modern ML and LLM tooling (e.g., PyTorch, TensorFlow, scikit-learn), including fine-tuning, evaluation, orchestration, and model serving You have practical experience building generative AI applications using embeddings, vector databases, RAG pipelines, agent workflows, prompt engineering, and guardrails You bring deep expertise in MLOps and platform engineering, including model registries, feature stores, CI/CD, infrastructure as code, experiment tracking, and automated validation You have a strong architectural understanding of distributed systems, event-driven services, streaming data, and cloud-native ML platforms, with the ability to optimize for performance, scalability, reliability, and cost You define robust evaluation and governance strategies, including offline benchmarking, online experimentation, hallucination analysis, model risk assessment, and responsible AI practicesAbout YouYou bring 7-9+ years of experience in software engineering and machine learning, with a track record of leading and delivering complex, production-scale AI systemsYou combine strong product judgment with technical depth, translating ambiguous business problems into scalable, high-impact AI solutions You thrive in complex, fast-moving environments and bring clarity, ownership, and strategic thinking to drive long-term platform successWhere You BelongYou will be part of a growing team of AI and industry experts dedicated to serving customers across the Kingdom of Saudi Arabia / United Arab Emirates. We are building a collaborative, high‑impact environment that brings together deep AI expertise, strong industry knowledge, and regional understanding to help customers innovate, transform, and lead in their markets.This team thrives on working together to turn complex business challenges into practical, scalable solutions. You will find an environment that values curiosity, ownership, and continuous learning, where new ideas are encouraged and real‑world impact matters. This is a place for people who enjoy building something new. You will thrive here if you are customer‑centric, motivated by meaningful outcomes, and comfortable navigating ambiguity in a fast‑evolving AI landscape.About The TeamWe are a team of engineers passionate about building valuable, scalable AI frameworks that drive real business impact. We thrive on solving challenging problems, innovating with cutting-edge technologies, and collaborating closely to deliver AI systems that are reliable, high-performing, and ready for production. Our team values ownership, technical excellence, and creating solutions that stand the test of scale and complexity.Bring out your bestSAP innovations help more than four hundred thousand customers worldwide work together more efficiently and use business insight more effectively. Originally known for leadership in enterprise resource planning (ERP) software, SAP has evolved to become a market leader in end-to-end business application software and related services for database, analytics, intelligent technologies, and experience management. As a cloud company with two hundred million users and more than one hundred thousand employees worldwide, we are purpose-driven and future-focused, with a highly collaborative team ethic and commitment to personal development. Whether connecting global industries, people, or platforms, we help ensure every challenge gets the solution it deserves. At SAP, you can bring out your best.We win with inclusionSAP’s culture of inclusion, focus on health and well-being, and flexible working models help ensure that everyone – regardless of background – feels included and can run at their best. At SAP, we believe we are made stronger by the unique capabilities and qualities that each person brings to our company, and we invest in our employees to inspire confidence and help everyone realize their full potential. We ultimately believe in unleashing all talent and creating a better world.SAP is committed to the values of Equal Employment Opportunity and provides accessibility accommodations to applicants with physical and/or mental disabilities. If you are interested in applying for employment with SAP and are in need of accommodation or special assistance to navigate our website or to complete your application, please send an e-mail with your request to Recruiting Operations Team: Careers@sap.com.For SAP employees: Only permanent roles are eligible for the SAP Employee Referral Program, according to the eligibility rules set in the SAP Referral Policy. Specific conditions may apply for roles in Vocational Training.Qualified applicants will receive consideration for employment without regard to their age, race, religion, national origin, ethnicity, gender (including pregnancy, childbirth, et al), sexual orientation, gender identity or expression, protected veteran status, or disability, in compliance with applicable federal, state, and local legal requirements.Successful candidates might be required to undergo a background verification with an external vendor.AI Usage in the Recruitment ProcessFor information on the responsible use of AI in our recruitment process, please refer to our Guidelines for Ethical Usage of AI in the Recruiting Process.Please note that any violation of these guidelines may result in disqualification from the hiring process.Requisition ID: 450236 | Work Area: Software-Design and Development | Expected Travel: 0 - 10% | Career Status: Professional | Employment Type: Regular Full Time | Additional Locations:

Welcome to MultiBank Group, a global financial pioneer established in 2005 in California and now proudly headquartered in Dubai, UAE. We specialize in delivering cutting-edge trading technology, unparalleled liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs.Join our thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$ 35 billion. As a heavily regulated institution with oversight from 18+ financial regulators across 5 continents, and recipient of over 80 financial awards, MultiBank Group is devoted to innovation, excellence, and empowering our clients to achieve their financial goals.Role OverviewWe are seeking a Senior Machine Learning Engineer to join our AI team as a technical owner of ML products and infrastructure. This is a deeply hands-on engineering position for someone who builds and scales production AI systems used by real users in real-time environments. The right candidate operates across the full ML lifecycle — from model design through deployment, optimization, and ongoing performance in production — and contributes to the technical direction of the AI platform.Key ResponsibilitiesArchitect and implement robust ML systems in production environments, ensuring scalability, reliability, and performance from day oneBuild and deploy supervised, unsupervised, deep learning, and generative AI models into live production environments at scaleOwn technical design for ML pipelines, feature stores, training infrastructure, and inference systems, driving decisions that balance performance, cost, and maintainabilityDesign and deliver RAG systems, fine-tuning pipelines, prompt engineering frameworks, and evaluation pipelines for production-grade LLM applicationsImplement and maintain CI/CD for ML, model versioning, monitoring, drift detection, and automated retraining pipelinesContinuously optimize model performance, inference latency, cost efficiency, and reliability across live systemsCollaborate with product managers, engineers, and data teams to translate business problems into scalable, maintainable AI solutionsMentor junior and mid-level ML engineers, establish best practices, and contribute to technical standards across the teamContribute to strategic decisions around data architecture, AI infrastructure, and cloud platform directionWork with mobile attribution and customer engagement data sources including Adjust, MoEngage, and Firebase for ML use cases such as churn prediction, personalization, and campaign optimizationRequirements7 to 15 or more years of experience in software engineering, data science, or ML engineeringStrong background in product companies, scale-ups, or enterprise AI platformsProven track record of building production-grade AI systems, not solely notebooks or proof-of-concept workComfortable owning systems end-to-end from data through model through deployment through monitoringProduct-first engineering approach, not research-only profilesAdvanced Python engineering skills with strong systems thinking and a focus on production qualityComfortable with fast iteration cycles and deploying models into live environmentsAbility to work directly and confidently with stakeholders and product ownersFintech or financial services experience is an advantageTechnical SkillsMachine Learning and AI: PyTorch, TensorFlow, XGBoost, LightGBM, Hugging Face (Transformers, Datasets, Diffusers)LLM and GenAI: OpenAI and Anthropic APIs, LangChain, LlamaIndex; RAG architectures with vector DB and retrieval pipelines; embedding models (OpenAI, Cohere, open-source); Pinecone, Weaviate, Milvus, FAISS; fine-tuning via LoRA and PEFT frameworks; evaluation using RAGAS and custom pipelinesMLOps and Production: Docker, Kubernetes, MLflow, Weights and Biases, Airflow, Dagster, Prefect, GitHub Actions, GitLab CI, Evidently AI, Arize, custom observability stacksCloud: AWS (SageMaker, EKS, S3, Lambda), Azure ML, Azure Databricks, GCPData Stack: Databricks, Spark, PySpark, Delta Lake, Apache Iceberg, Lakehouse architecturesWhy Join Us?Work with one of the world’s leading financial derivatives institutions. Competitive salary plus performance-based incentives. Access to a dynamic, international, and fast-growing environment. Strong opportunities for career progression within a global financial group. Be part of a business committed to innovation, excellence, and long-term growth. Become part of our international community at MultiBank Group, dedicated to excellence, innovation, and shaping the future of finance.MultiBank Group is an equal opportunity employer. We welcome applications from candidates of all backgrounds and do not discriminate on the basis of nationality, gender, age, religion, or disability.

Welcome to MultiBank Group, a global financial pioneer established in 2005 in California and now proudly headquartered in Dubai, UAE. We specialize in delivering cutting-edge trading technology, unparalleled liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs.Join our thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$ 35 billion. As a heavily regulated institution with oversight from 18+ financial regulators across 5 continents, and recipient of over 80 financial awards, MultiBank Group is devoted to innovation, excellence, and empowering our clients to achieve their financial goals.Role OverviewWe are seeking a Senior Machine Learning Engineer to join our AI team as a technical owner of ML products and infrastructure. This is a deeply hands-on engineering position for someone who builds and scales production AI systems used by real users in real-time environments. The right candidate operates across the full ML lifecycle — from model design through deployment, optimization, and ongoing performance in production — and contributes to the technical direction of the AI platform.Key ResponsibilitiesArchitect and implement robust ML systems in production environments, ensuring scalability, reliability, and performance from day oneBuild and deploy supervised, unsupervised, deep learning, and generative AI models into live production environments at scaleOwn technical design for ML pipelines, feature stores, training infrastructure, and inference systems, driving decisions that balance performance, cost, and maintainabilityDesign and deliver RAG systems, fine-tuning pipelines, prompt engineering frameworks, and evaluation pipelines for production-grade LLM applicationsImplement and maintain CI/CD for ML, model versioning, monitoring, drift detection, and automated retraining pipelinesContinuously optimize model performance, inference latency, cost efficiency, and reliability across live systemsCollaborate with product managers, engineers, and data teams to translate business problems into scalable, maintainable AI solutionsMentor junior and mid-level ML engineers, establish best practices, and contribute to technical standards across the teamContribute to strategic decisions around data architecture, AI infrastructure, and cloud platform directionWork with mobile attribution and customer engagement data sources including Adjust, MoEngage, and Firebase for ML use cases such as churn prediction, personalization, and campaign optimizationRequirements7 to 15 or more years of experience in software engineering, data science, or ML engineeringStrong background in product companies, scale-ups, or enterprise AI platformsProven track record of building production-grade AI systems, not solely notebooks or proof-of-concept workComfortable owning systems end-to-end from data through model through deployment through monitoringProduct-first engineering approach, not research-only profilesAdvanced Python engineering skills with strong systems thinking and a focus on production qualityComfortable with fast iteration cycles and deploying models into live environmentsAbility to work directly and confidently with stakeholders and product ownersFintech or financial services experience is an advantageTechnical SkillsMachine Learning and AI: PyTorch, TensorFlow, XGBoost, LightGBM, Hugging Face (Transformers, Datasets, Diffusers)LLM and GenAI: OpenAI and Anthropic APIs, LangChain, LlamaIndex; RAG architectures with vector DB and retrieval pipelines; embedding models (OpenAI, Cohere, open-source); Pinecone, Weaviate, Milvus, FAISS; fine-tuning via LoRA and PEFT frameworks; evaluation using RAGAS and custom pipelinesMLOps and Production: Docker, Kubernetes, MLflow, Weights and Biases, Airflow, Dagster, Prefect, GitHub Actions, GitLab CI, Evidently AI, Arize, custom observability stacksCloud: AWS (SageMaker, EKS, S3, Lambda), Azure ML, Azure Databricks, GCPData Stack: Databricks, Spark, PySpark, Delta Lake, Apache Iceberg, Lakehouse architecturesWhy Join Us?Work with one of the world’s leading financial derivatives institutions. Competitive salary plus performance-based incentives. Access to a dynamic, international, and fast-growing environment. Strong opportunities for career progression within a global financial group. Be part of a business committed to innovation, excellence, and long-term growth. Become part of our international community at MultiBank Group, dedicated to excellence, innovation, and shaping the future of finance.MultiBank Group is an equal opportunity employer. We welcome applications from candidates of all backgrounds and do not discriminate on the basis of nationality, gender, age, religion, or disability.

We're looking for a motivated AI & IoT Intern to help build AI-powered applications that combine software, sensors, edge devices, and automation. You'll work on real-world projects involving AI agents, computer vision, IoT, embedded systems, and control engineering. Responsibilities Develop AI and IoT prototypes Build applications using Python and LLM APIs Design and simulate control systems using MATLAB/Simulink Connect and integrate sensors, cameras, and edge devices Analyse IoT data and develop AI models Assist with computer vision, automation, and embedded systems projects Test, document, and present solutions What You'll Gain Hands-on experience with AI agents, IoT, computer vision, edge AI, and MATLAB/Simulink Exposure to real-world engineering and product development projects Mentorship from experienced AI and IoT engineers Opportunity to contribute to production AI solutions A strong portfolio of practical AI and IoT projects