Senior/Principal ML Scientist, Translational Biology

Lila Sciences
San Francisco
Workplace: OnsiteFull timeUSD 268,000 - 384,000 annuallyFunction: Research & Scientific (R&D)Education: phdSkills: ["Evidence judgment","Reproducible evaluation","Mechanistic reasoning","Communication","Cross-functional collaboration"]

Build systems that connect mechanistic models and structured biological evidence to human clinical programs. Assess whether interventions’ mechanisms are established, assumed, or unexamined by analyzing human genetics, expression, cohorts, and trial-derived data. Evaluate trial design (endpoints, timing, biomarkers, dosing, eligibility/enrichment) and create evaluation frameworks with outcome-verifiable forecasts. Co-design with ML scientists and publish findings.

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Lila Sciences
Lila Sciences
2 days ago

Senior/Principal ML Scientist, Translational Biology

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Last checked: 55 minutes agoStatus: Live

Job Summary

Build systems that connect mechanistic models and structured biological evidence to human clinical programs. Assess whether interventions’ mechanisms are established, assumed, or unexamined by analyzing human genetics, expression, cohorts, and trial-derived data. Evaluate trial design (endpoints, timing, biomarkers, dosing, eligibility/enrichment) and create evaluation frameworks with outcome-verifiable forecasts. Co-design with ML scientists and publish findings.
Location: San Francisco
Workplace: Onsite
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Build systems that assess mechanistic support for clinical programs by translating structured evidence and mechanistic models into established/assumed/unexamined judgments.
  • •Ground mechanisms in human data by analyzing genetics, expression, cohort, and trial-derived evidence to determine whether mechanisms are present, active, and rate-limiting across relevant patient populations.
  • •Assess trial design against mechanisms by evaluating endpoint choice/timing, biomarker definition/assay, dose/schedule, and eligibility/enrichment criteria; encode assessments for scalable application.
  • •Build and run evaluation frameworks that forecast real program progression using only information available at the prediction date, with evidence boundary enforcement; report when contributions add nothing.
  • •Co-design with ML scientists, mechanism scientists, and engineers to translate model outputs into decisions and publish results; communicate findings to technical and cross-functional audiences.

Pay and Benefits

Salary: USD 268,000 - 384,000 annually
Equity and Bonus:Equity
Perks:Health InsuranceDentalVisionLife InsuranceDisability InsurancePaid LeaveParental LeaveCommuter BenefitsBike ShareMeal AllowanceFlexible Time

Key Requirements

  • •PhD in a computational discipline (e.g., translational bioinformatics, computational biology, biomedical informatics, biostatistics, epidemiology, machine learning) with research centered on human biomedical data.
  • •Hands-on ML and data analysis for translational medicine, with fluent Python and experience in reproducible pipelines analyzing human genetic, multi-omic, cohort, trial, or real-world data.
  • •Mechanistic reasoning about therapeutic interventions, including stating how an intervention is meant to work and what evidence would establish each step.
  • •Clinical development fluency: working knowledge of trial design, endpoints, biomarker strategy, eligibility, and enrichment to judge whether a protocol tests its claimed mechanism.
  • •Evidence judgment that resists hindsight, making judgments reproducible through structured evaluation rather than case-by-case expertise.
Education:PhD / Doctorate in Computational discipline (translational bioinformatics, computational biology, biomedical informatics, biostatistics, epidemiology, machine learning, or related)
Skills:Evidence judgmentReproducible evaluationMechanistic reasoningCommunicationCross-functional collaboration
Languages:English
Tech Stack:Python

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
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