Senior ML Scientist, Biological Systems

Lila Sciences
San Francisco
Full timeUSD 268,000 - 358,000 annuallyFunction: Research & Scientific (R&D)Education: phdSkills: ["Communication","Cross-functional collaboration","Research rigor","Problem-solving","Scientific reasoning"]

Build domain models for perturbation, genetics, and multimodal experimental data, translating biological questions into rigorous ML formulations. Develop interpretable, mechanism-informed representations and deliver calibrated uncertainty for predictions into unmeasured conditions. Pre-register and beat baselines, partner with experimental scientists on data generation and validation, and design benchmarks tied to biological and therapeutic outcomes. Support integration of domain models into agentic workflows.

Loading

Loading job details...

Preparing the role view and application actions.

FursaFursa
Lila Sciences
Lila Sciences
2 days ago

Senior ML Scientist, Biological Systems

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 11 hours agoStatus: Live
Reposted: similar role first listed 2 months ago

Job Summary

Build domain models for perturbation, genetics, and multimodal experimental data, translating biological questions into rigorous ML formulations. Develop interpretable, mechanism-informed representations and deliver calibrated uncertainty for predictions into unmeasured conditions. Pre-register and beat baselines, partner with experimental scientists on data generation and validation, and design benchmarks tied to biological and therapeutic outcomes. Support integration of domain models into agentic workflows.
Location: San Francisco
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Build domain models for perturbation, genetic, and multimodal experimental data and formulate rigorous ML problem statements from biological questions.
  • •Create interpretable, mechanism-informed models where supported by biology, with parameters a biologist can interpret.
  • •Make uncertainty a deliverable by producing calibrated posteriors, honest error bars, and useful information for downstream decision-making.
  • •Partner with experimental scientists to guide what is measured, in which contexts, and at what precision, and develop experiment-selection methods to reduce uncertainty.
  • •Design benchmarks and support integration of domain models into agentic workflows so predictions drive next experiments and mechanistic downstream reasoning.

Pay and Benefits

Salary: USD 268,000 - 358,000 annually
Equity and Bonus:Equity
Perks:Health InsuranceDentalVisionLife InsuranceDisability InsuranceParental LeaveLearning BudgetMeal Allowance

Key Requirements

  • •PhD in machine learning, statistics, computational biology, computer science, bioengineering, physics, or a related quantitative field, with a strong publication record or equivalent impact.
  • •Experience developing models for high-dimensional biological data and connecting ML methods to biological mechanism and experimental design.
  • •Track record of generalization under structured sparsity, including predicting into conditions not directly observed.
  • •Strong grounding in uncertainty and evaluation rigor, including calibration and evaluation design.
  • •Strong programming skills and clear communication across ML, biology, and experimental teams, including leading ambiguous research problems end-to-end.
Education:PhD / Doctorate
Skills:CommunicationCross-functional collaborationResearch rigorProblem-solvingScientific reasoning
Languages:English

Company Brief

Lila Sciences
Develops AI-driven platforms to accelerate drug discovery and biological research by integrating machine learning with chemical and biological data to predict molecular properties, streamline candidate selection, and enable faster therapeutic development.
Industry: Biotech
Company Size: Small (11 to 50 employees)
Growth: Early Stage Startup
Funding: Seed
WebsiteLinkedIn