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Fuse Energy is an AI-first energy company building a fully integrated energy system combining solar, batteries, grid infrastructure, power trading, and distributed energy solutions. The company has raised over $200M from top-tier investors including Balderton, Accel, Lakestar, and others, with strategic angels from Meta, Revolut, Spotify, and Uber.
As an Applied AI Researcher, you will design and execute experiments to identify optimal models and approaches for various workloads, balancing accuracy, latency, and cost. You'll own the evaluation framework, building high-quality datasets and golden answers while combining human and LLM judgments to measure evaluator confidence. Your work will span post-training methods including supervised fine-tuning and reinforcement learning, as well as building reliable agent harnesses for long-running tasks with tool use, state tracking, retries, and appropriate human approvals.
You'll set evaluation and escalation thresholds based on failure consequences and collaborate across Fuse teams to translate research into production systems. This role sits at the intersection of frontier AI research and practical implementation, bringing cutting-edge methods into a company operating across the full energy stack—from customer-facing software to physical infrastructure and real-time power trading.
REQUIREMENTS:
- Strong experience applying modern AI models to real-world tasks with a proven track record of improving measured outcomes through experimentation
- Deep understanding of evaluations, dataset quality, and failure analysis; ability to distinguish genuine improvements from unreliable benchmarks
- Hands-on experience with 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 regarding deterministic checks and human review needs
- Ability to move fluidly between research and implementation: formulate hypotheses, run rigorous tests, inspect failures, and ship working solutions
- Experience at a frontier AI lab or equivalent team operating at a similar level of experimentation is especially valuable
