Senior ML Scientist, Biological Systems

What you'll do

• Build and evaluate models for perturbation, genetic, and multimodal experimental data; connect predictions to biological mechanisms and therapeutic opportunities.
• Partner with experimental scientists to guide data generation, validation, and experiment selection; design benchmarks and prioritize targets and mechanisms.
• Support domain-model integration into agentic workflows, including tool deployment and agent-harness development.

What they ask for

• PhD in a related quantitative field, with a strong publication record or equivalent industry impact.
• Experience developing models for high-dimensional biological data and connecting ML methods to biological mechanism and experimental design.
• Track record modeling generalization to unobserved conditions, such as sparse designs, held-out combinations, or new contexts.
• Expertise in uncertainty calibration and rigorous evaluation, plus strong programming, ML research workflows, communication, and research leadership.

Nice to have

• Mechanistic or probabilistic modeling, such as Bayesian hierarchical models, simulation-based inference, state-space or ODE-based models, or mechanism-informed ML.
• Experience with active learning, Bayesian optimal experimental design, closed-loop experimentation, or deploying models in scientific decision workflows.

Pay

$268,000 — $358,000 USD (base salary)

Tools

Bayesian hierarchical models, state-space, ODE-based models, neural differential equations

AI focus

Build domain models that support scientific reasoning and experimental decisions, and integrate them into agentic workflows and environments for training reasoning models.