- Location
- London, England, United Kingdom · published · San Francisco, California, United States · Zürich, Zurich, Switzerland · Dubai, Dubai, United Arab Emirates · New York, New York, United States · Germany
- Type
- Full-time
- Department
- Engineering
- Source
- Workable
Description
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. As an Applied AI Engineer, this role suits someone who is specifically interested in applied AI and how it can improve the energy experience for our customers and our internal operations.
You’ll work on consumer features (using VLM and LLM tools), 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.