Senior/Principal ML Scientist, Translational Biology
$268k - $384k • San Francisco, CA USA • Full-time
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Lila is revolutionizing biomedicine by integrating large-scale automated data generation with advanced AI. We are building a system where AI, automation, and experimental biology work together synergistically. This role focuses on connecting that work to human medicine by developing systems that evaluate the biological validity of clinical programs. You will build the infrastructure to assess mechanistic support, ground biological mechanisms in human data, and evaluate clinical trial designs against proposed mechanisms. The goal is to create scalable systems that can rigorously assess and forecast program outcomes, ensuring scientific integrity and enabling public dissemination of findings.
Responsibilities
- Build systems to assess the mechanistic support for clinical programs using biological evidence and mechanistic models.
- Analyze human genetic, expression, cohort, and trial data to determine if a mechanism is present, active, and rate-limiting in patient populations.
- Evaluate clinical trial designs, including endpoints, biomarkers, and enrollment criteria, for alignment with proposed mechanisms and encode this for scalable application.
- Develop evaluations and outcome-verifiable forecasts to hold systems accountable, using only information available at the prediction date.
- Co-design with ML scientists, mechanism scientists, and engineers to translate model outputs into actionable decisions.
- Communicate findings clearly to technical and cross-functional audiences, including scientists, engineers, product partners, and therapeutic stakeholders.
- Support external scientific visibility through publications, presentations, and engagement with relevant scientific communities.
Requirements
- PhD in a computational discipline such as translational bioinformatics, computational biology, biomedical informatics, biostatistics, epidemiology, or machine learning, with a research focus on human biomedical data.
- Hands-on experience with ML and data analysis for translational medicine, including fluent Python and substantial experience analyzing human genetic, multi-omic, cohort, trial, or real-world data in reproducible pipelines.
- Ability to perform mechanistic reasoning about therapeutic interventions, including understanding how an intervention is meant to work and where human evidence supports or contradicts it.
- Working knowledge of clinical development, including trial design, endpoints, biomarker strategy, eligibility, and enrichment.
- Strong evidence judgment and a systems instinct, with the ability to reason about what was knowable at a given time and resist hindsight bias.
- Experience with human genetics for target identification and validation (bonus).
- Experience with biomarker development, patient stratification, companion diagnostics, or enrichment strategy (bonus).
- Experience with clinical trial datasets, real-world data, or observational cohort analysis (bonus).
- Experience evaluating language models or agents on scientific judgment tasks, including contamination and memorization controls (bonus).
- Familiarity with survival analysis, competing risks, calibration, or forecasting methodology (bonus).
- Experience with structured evidence frameworks like GRADE or systematic review protocols (bonus).
Benefits
- Competitive base compensation
- Bonus potential
- Generous early-stage equity
- Medical coverage
- Dental coverage
- Vision coverage
- Employer-paid life insurance
- Employer-paid disability insurance
- Flexible time off
- Generous company-wide holidays
- Paid parental leave
- Educational assistance program
- Commuter benefits
- Bike share memberships
- Company subsidized lunch program