Senior Machine Learning Scientist I/II, Model-Driven Optimization

Highlights

• Build closed-loop ML systems that decide what proteins to design and test next.
• Advance multi-objective optimization for therapeutic protein discovery and design.
• Shape data strategies that make experimental campaigns maximally informative.

What they offer

• annual bonus
• equity compensation
• competitive benefits package

Ideal candidate

Creative, rigorous and execution-oriented ML scientist who thrives at the biology interface and turns uncertain experimental loops into reliable systems that accelerate therapeutic discovery.

Requirements

• PhD in machine learning, computational biology, computer science, applied mathematics, engineering, or a related quantitative field
• Strong practical experience with probabilistic machine learning, Bayesian optimization, active learning, experimental design, or related approaches for sequential decision-making under uncertainty
• Experience developing machine learning methods or systems for biological, biomedical, or experimental scientific data
• Ability to reason about noisy assays, sparse labels, experimental bias, and data-generation strategy
• Demonstrated ability to translate ML ideas into systems, tools, or workflows that affect real scientific, experimental, or product decisions
• Strong Python skills and experience with modern ML frameworks such as PyTorch, JAX, or similar tools