Deep Learning Quantitative Researcher

Please submit resumes to QuantTalentEUR@mlp.com and reference REQ-30088.Preferred Candidate Profile Top-tier academic background from a globally top-20 university (e.g., MIT, Harvard, Princeton,Stanford, Caltech) PhD-level training in Computer Science, Engineering, Physics, Mathematics, or StatisticsPreferred Gold medal in a national or international olympiad (IMO, CMO, IOI, NOI, IPhO, CPhO)strongly preferred Practical, hands-on experience with large-scale, end-to-end deep learning at a top-tier quantitativeTrading Firm Or a Leading AI/technology Company PreferredKey Responsibilities Design and build the firm’s core deep learning pipelines for applied quantitative alpha research—from data preparation and distributed training through evaluation and production deployment. Drive a significant part of the research agenda using applied deep learning techniques, owning thefull empirical loop: problem formulation, model design, training, validation, and performanceattribution. Uphold rigorous research discipline in a low signal-to-noise domain — strict out-of-samplehygiene, leakage prevention, and honest benchmarking against simpler baselines. Act as the firm’s central point of deep learning expertise: advise on architecture selection andtraining diagnostics, review model designs, and set standards for how models are evaluatedand promoted. Facilitate the seamless flow of model fitting and model computation across teams and systemsthrough standardized training and inference interfaces and reusable components.Qualifications & Experience 3–5 years of professional experience applying deep learning to large-scale problems, ideally inquantitative finance. A strong PhD research record plus hands-on experience training largemodels at a leading AI/technology company will be considered in lieu of direct quant experience. Proven end-to-end ownership of the deep learning model lifecycle on at least one significantproduction system or published research line. Deep expertise in Python and a modern DL framework. Hands-on experience with large-scale model training: distributed/multi-GPU training,mixed precision, and throughput profiling and optimization. Strong foundations in statistics, optimization, and machine learning theory.Hard Skills & Technical Knowledge Command of modern deep learning architectures, and the judgment to know when a simplermodel should win. Practical technique for low signal-to-noise learning: regularization, ensembling, and validationprotocols that survive out-of-sample. Experience with large-scale datasets — efficient columnar formats, streaming data loaders,and point-in-time-correct dataset construction. Fluency with experiment-management tooling: experiment tracking, hyperparameter optimization,and reproducible research environments. Working knowledge of C++ or CUDA-level optimization a plus; familiarity with LLM toolingas a research accelerant a plus.Soft Skills Research Taste & Rigor: Designs clean experiments and kills ideas quickly when theevidence says so. Proactive Collaboration: Builds strong partnerships across research and engineering. High Integrity: Upholds rigorous ethical standards in handling sensitive data and models. Growth Mindset: Stays current with a fast-moving field and adopts what works. Superb Communication: Explains model behavior and uncertainty to technical and nontechnicalaudiences.