Fuse is an AI-first energy company 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, 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.
Energy is a rare place to apply AI across the full stack: from helping customers and building software to operating physical infrastructure and trading power. Our ambition is to make energy cheaper and more abundant with AI, while building the energy and compute infrastructure that the next generation of AI will need.
Responsibilities
- Design experiments to find the best model and approach for each workload, balancing accuracy, latency and cost.
- Own our evaluation framework: build high-quality datasets and golden answers, combine human and LLM judgments and measure how much confidence to place in each evaluator.
- Explore post-training methods, including supervised fine-tuning and reinforcement learning.
- Build agent harnesses that make long-running tasks reliable: tool use, deterministic checks, state tracking, retries, resource allocation and appropriate human approvals.
- Set evaluation and escalation thresholds according to the consequences of failure.
- Work with teams across Fuse to turn research results into production systems and bring new methods and hard-won lessons from the frontier of applied AI into the company.
Requirements
- Strong experience applying modern AI models to real-world tasks with a track record of improving measured outcomes through experimentation.
- Deep understanding of evaluations, dataset quality and failure analysis. You know how to distinguish a genuine improvement from an unreliable benchmark result.
- Hands-on experience with several of model selection and routing, fine-tuning, reinforcement learning, agent systems or computer use.
- Strong software engineering skills, including Python and the ability to build reliable experimental and production tooling.
- Good judgment about cost, latency, reliability and risk, especially when deciding where deterministic checks or human review are needed.
- Ability to move between research and implementation: formulate a hypothesis, run a rigorous test, inspect failures and ship what works.
- Experience at a frontier AI lab or a team operating at a similar level of experimentation would be especially valuable.
Benefits
- Competitive salary and eligibility for equity
- Biannual bonus scheme
- Fully expensed tech to match your needs
- Private health insurance
- Breakfast and dinner allowance for office-based employees
As we hire globally, benefits may vary by location.