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.

Responsibilities

Ship AI features for customers, such as personalised energy recommendations and onboarding from an energy bill.

Build 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 should.

Own evaluation: datasets, golden answers, human and LLM judges, A/B tests. Know whether a change genuinely helped.

Choose models by performance, latency and cost, and revisit as models improve.

Work with trading, operations and backend teams so models reflect real-time market conditions, energy pricing and demand and weather forecasts.

Monitor AI systems in production and keep them reliable.

Requirements

3+ years of experience, with requirements varying by role level.

Experience 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.

A software engineer who moved into applied AI, or an ML engineer who writes production code. We value both.

Strong Python (or similar) and experience deploying LLMs or VLMs into production.

Solid understanding of cloud technologies, containerisation and scalable AI applications.

Analytical, rigorous and quick to learn. You don’t need our exact tools, just the ability to pick them up.

Bonus: PyTorch or TensorFlow; forecasting, weather and demand modelling; energy markets and trading; fine-tuning or RL; vision, document or NLP pipelines; agent runtimes.

Breakfast and dinner allowance for office-based employees

As we hire globally, benefits may vary by location.