Applied AI Engineer

Fuse Energy is an energy startup on a mission to make energy abundant and affordable, fast. We combine first-principles thinking with cutting-edge technology to build a radically better energy system.We've raised over $200M from top-tier investors including Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, 20VC, Hummingbird and Collaborative Fund, alongside strategic angels including Nico Rosberg and GPs behind Meta, Revolut, Spotify and Uber.We're building a fully integrated energy company: developing our own solar, batteries and other generation projects, building our own hardware, improving and developing grid infrastructure, trading power in real time, using AI across the business, and installing distributed energy in homes. By selling directly to consumers we cut out the middleman, lower costs and pass the savings on to our customers.We're building a cutting-edge AI team, and this role is for an engineer who wants to apply AI to real problems in energy. As an Applied AI Engineer, you'll help build our Product Factory: an agentic pipeline that takes software from requirements and design through code review, deployment and monitoring, with human review where the risk calls for it.You'll also ship customer-facing features using VLMs and LLMs, and build AI tools and agents that make teams across Fuse more productive, from customer support to warehouses and back office. This is an applied engineering role, not pure research: you'll own what you build, from spec to production.ResponsibilitiesShip AI features for customers, such as personalised energy recommendations and onboarding from an energy billBuild LLM applications and agents for support, engineering, warehouse and back-office teams, including multi-agent systems and durable workflows (e.g. Temporal)Build the engineering around them: cloud infrastructure, integrations, and access control so users and agents only reach what they shouldOwn evaluation: datasets, golden answers, human and LLM judges, A/B tests. Know whether a change genuinely helpedChoose models by performance, latency and cost, and revisit as models improveWork with trading, operations and backend teams so models reflect real-time market conditions, energy pricing and demand and weather forecastsMonitor AI systems in production and keep them reliableRequirements3+ years of experience, with requirements varying by role levelExperience owning an applied AI system end to end, ideally in a startup or small team, and able to explain what broke and how you knew it workedA software engineer who moved into applied AI, or an ML engineer who writes production code. We value bothStrong Python (or similar) and experience deploying LLMs or VLMs into productionSolid understanding of cloud technologies, containerisation and scalable AI applicationsAnalytical, rigorous and quick to learn. You don't need our exact tools, just the ability to pick them upBonus: PyTorch or TensorFlow; forecasting, weather and demand modelling; energy markets and trading; fine-tuning or RL; vision, document or NLP pipelines; agent runtimesBenefitsCompetitive salary and eligibility for equityBiannual bonus schemeFully expensed tech to match your needsPrivate health insuranceBreakfast and dinner allowance for office-based employeesAs we hire globally, benefits may vary by location.