Applied AI Engineer

pFuse 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. /ppWe'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. /ppWe'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. /ppWe’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 bProduct Factory /b: 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.br/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.br/ /ph3Responsibilities /h3ulliShip AI features for customers, such as personalised energy recommendations and onboarding from an energy bill. /liliBuild LLM applications and agents for support, engineering, warehouse and back-office teams, including multi-agent systems and durable workflows (e.g. Temporal). /liliBuild the engineering around them: cloud infrastructure, integrations, and access control so users and agents only reach what they should. /liliOwn evaluation: datasets, golden answers, human and LLM judges, A/B tests. Know whether a change genuinely helped. /liliChoose models by performance, latency and cost, and revisit as models improve. /liliWork with trading, operations and backend teams so models reflect real-time market conditions, energy pricing and demand and weather forecasts. /liliMonitor AI systems in production and keep them reliable. /li /ululli3+ years of experience, with requirements varying by role level. /liliExperience 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 worked. /liliA software engineer who moved into applied AI, or an ML engineer who writes production code. We value both. /liliStrong Python (or similar) and experience deploying LLMs or VLMs into production. /liliSolid understanding of cloud technologies, containerisation and scalable AI applications. /liliAnalytical, rigorous and quick to learn. You don’t need our exact tools, just the ability to pick them up. /lilibBonus: /b PyTorch or TensorFlow; forecasting, weather and demand modelling; energy markets and trading; fine-tuning or RL; vision, document or NLP pipelines; agent runtimes. /li /ululliCompetitive salary and eligibility for equity /liliBiannual bonus scheme /liliFully expensed tech to match your needs /liliPrivate health insurance /liliBreakfast and dinner allowance for office-based employees /li /ulpAs we hire globally, benefits may vary by location. /p