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About the Role

You will work on the biological side of our RL environments and agents, starting with proteins. You will help build the environments themselves, working with our scientists on what goes into them, and build the harnesses through which agents use biological models: integrating the best public protein models, making them available as tools, and testing which ones help an agent and when their predictions can be trusted.

Responsibilities

Help build biological RL environments.
Integrate public protein models as tools for agents.
Test and validate protein model performance and trustworthiness.
Fine-tune or post-train existing models on experimental data.
Run scaling and data-mix experiments to improve models.
Translate experimental results into recommendations for future measurements.
Write clear documentation on modeling choices, results, and limitations.

Requirements

Strong background in machine learning for biology.
Experience building and training ML models for biology.
Experience adapting existing ML models and testing results against wet-lab data.
Understanding of model performance on held-out splits versus real-world lab performance.
Experience with protein language, structure, and affinity models (e.g., ESM C, Boltz-2, OpenFold3, AlphaFold 3).
Experience fine-tuning or post-training pretrained biological models on experimental data.
Experience validating model results against wet-lab measurements.
Experience building RL environments, agent harnesses, or tool interfaces for LLM agents.
Experience working with noisy experimental data, including replicates, controls, and batch effects.
Experience running experiments on data, such as scaling studies, data-mix ablations, and learning curves.
Clear writing and communication skills for ML and scientific audiences.
Strong Python and PyTorch or JAX skills.
Daily use of coding agents.

Skills

Machine Learning for Biology
Model Building and Training
Model Adaptation
Wet-lab Data Validation
Protein Language Models
Protein Structure Models
Protein Affinity Models
Fine-tuning Pretrained Models
Post-training Models
Experimental Data Analysis
RL Environments
Agent Harnesses
LLM Agent Integration
Noisy Data Handling
Scaling Studies
Data-mix Ablations
Learning Curves
Technical Writing
Scientific Communication
Python
PyTorch
JAX
Coding Agents
Reinforcement Learning (Nice to have)
Antibody Design (Nice to have)
Binder Design (Nice to have)
Functional Genomics Data (Nice to have)
Single-cell Data (Nice to have)
Biological Machine Learning Publications (Nice to have)
Open-source Biological ML Work (Nice to have)

Location

London
San Francisco

Work Type

Full-time

Experience Level

Mid-level
Senior

Education Level

Degree in a relevant field

Benefits

30 days of annual leave plus local public holidays
UK: Pension with 10% employer contribution
UK: Bupa private health cover
US: 401(k) with matching
US: Health cover

About the Company

Stealth startup in AI and bio.
Building infrastructure for data generation.
Focusing on AI for biology's data problem.
Building infrastructure to produce critical biological data.
Emphasis on quality and traceability in data generation.

Equal Opportunity

Equal opportunity employer.
Hiring decisions based on merit, scope-fit, and expected working relationship.
Applications welcome from candidates of any background.