Lead Applied AI Researcher

Fuse Energy 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.

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