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About the Role
You will own how our agents learn, building the harnesses and RL environments they work in and post-training the models that run through them. Our agents work on long-horizon scientific problems, reading papers, querying databases, running code and calling biological models to decide what to test next, and they also need to notice when an experiment has gone wrong. You will also build environments where agents carry out experimental work, not only plan it. Rewards in science are noisy, expensive and slow to arrive, so careful reward design matters more than the scale of a run. When an agent improves, you will work out whether that came from the model itself or from a change in the harness, the tools or the task. You will report into the intelligence team and work closely with the Bio ML researchers, the evaluation and infrastructure engineers, our software engineers and our scientists.
Responsibilities
- Build harnesses and RL environments for agents.
- Post-train models that run through RL environments.
- Build environments where agents carry out experimental work.
- Design learning signals for long trajectories.
- Perform rigorous comparisons of experiments, including matched compute, repeated runs, noise estimates, ablations, and held-out problems.
- Communicate experiment design, results, and limitations clearly.
Requirements
- Trained or post-trained language models and agents with reinforcement learning.
- Built tasks, rewards, harnesses, and evaluations around RL agents.
- Read traces before trusting an average.
- Start from a baseline you trust.
- Hands-on experience training LLMs with reinforcement learning, using methods such as PPO or GRPO, including reward design and detecting reward hacking.
- Hands-on experience with RL training frameworks such as veRL, Miles or TRL.
- Hands-on experience with RL environment frameworks such as Harbor or Prime Intellect's verifiers.
- Hands-on experience with inference engines such as vLLM or SGLang.
- Experience building agent harnesses and long-horizon tasks with tool use, such as search, code execution and external APIs.
- Experience designing learning signals for long trajectories, including verifiers, LLM judges, process reward models and credit assignment across steps.
- Experience with rigorous comparisons, with matched compute, repeated runs, noise estimates, ablations and held-out problems.
- Clear writing and communication about experiment design, results and what they do and do not show.
- Strong Python and PyTorch skills.
- Daily use of coding agents.
Skills
- Reinforcement Learning (RL)
- LLM Training
- Agent Training
- Reward Design
- Reward Hacking Detection
- RL Training Frameworks (veRL, Miles, TRL)
- RL Environment Frameworks (Harbor, Prime Intellect's verifiers)
- Inference Engines (vLLM, SGLang)
- Agent Harnesses
- Long-Horizon Tasks
- Tool Use (Search, Code Execution, External APIs)
- Learning Signals Design
- Verifiers
- LLM Judges
- Process Reward Models
- Credit Assignment
- Experiment Design
- Results Communication
- Python
- PyTorch
- Coding Agents
- Applying RL/Agents to Biological Problems (ProtRL)
- Multi-Agent Setups (Proposer/Critic, Debate)
- Learning from Noisy/Delayed Rewards
- Model Confidence Calibration
- Publications/Open-Source Work/Public Benchmarks in RL/Agents
Location
- London
- San Francisco
Work Type
- Full-time
Experience Level
- Mid-level
- Senior
Benefits
- 30 days of annual leave plus local public holidays
- Pension with 10% employer contribution (UK)
- Bupa private health cover (UK)
- 401(k) with matching (US)
- Health cover (US)
About the Company
- Stealth startup in AI and bio, building infrastructure for data generation.
- AI for biology has a data problem; building infrastructure to produce the data that matters most.
- Quality and traceability are built in from day one.
- Heads-down on execution while in stealth.
Equal Opportunity
- Equal opportunity employer.
- Hiring decisions made on merit, scope-fit, and the strength of the working relationship.
- Applications welcome from candidates of any background.