Scientist II/ Senior Scientist, BioML

Lila Sciences
San Francisco
Workplace: OnsiteFull timeUSD 228,000 - 358,000 annuallyFunction: Research & Scientific (R&D)Education: phdSkills: ["Scientific judgment","Communication","Cross-functional collaboration","Reproducible analysis","Data interpretation"]

Build mechanistic reasoning and evaluation systems that translate biological hypotheses into structured, testable frameworks. Analyze single-cell, perturbation, proteomics imaging, and genetic data to generate model-ready evidence and assess data quality for decision-making. Design experimental campaigns to ground systems in real measurements, produce outcome-verifiable benchmarks, and partner with ML researchers, engineers, and experimental scientists. Communicate results across scientific, engineering, and therapeutic audiences.

Loading

Loading job details...

Preparing the role view and application actions.

FursaFursa
Lila Sciences
Lila Sciences
5 days ago

Scientist II/ Senior Scientist, BioML

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI
Last checked: 8 hours agoStatus: Live

Job Summary

Build mechanistic reasoning and evaluation systems that translate biological hypotheses into structured, testable frameworks. Analyze single-cell, perturbation, proteomics imaging, and genetic data to generate model-ready evidence and assess data quality for decision-making. Design experimental campaigns to ground systems in real measurements, produce outcome-verifiable benchmarks, and partner with ML researchers, engineers, and experimental scientists. Communicate results across scientific, engineering, and therapeutic audiences.
Location: San Francisco
Workplace: Onsite
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Encode mechanistic reasoning into systems by building structured frameworks and evaluation workflows that score whether answers are reached for the right reason.
  • •Analyze biological data (single-cell, perturbation, proteomics imaging, and genetics) by building pipelines that transform raw measurements into model-ready evidence, including QC and annotation-stability analyses.
  • •Design experimental campaigns to anchor systems in real measurements and to generate outcome-verified cases for training and scoring.
  • •Build outcome-verifiable benchmarks with evidence boundaries (e.g., retrodiction sets) and define direct vs. inferred evidence for system scoring.
  • •Co-design evaluations with ML scientists and engineers and communicate results by publishing and presenting to scientific, engineering, and therapeutic audiences.

Pay and Benefits

Salary: USD 228,000 - 358,000 annually
Equity and Bonus:Equity
Perks:Health InsuranceDentalVisionLife InsuranceDisability InsurancePaid ParentalCommuter BenefitsMeal Allowance

Key Requirements

  • •PhD in Computational Biology, Bioinformatics, Computational Genomics, Biostatistics, Machine Learning, or a related quantitative field, with research centered on biological data.
  • •Strong hands-on computational and data-analysis depth; fluent Python and experience analyzing high-dimensional biological data in reproducible, version-controlled pipelines.
  • •Mechanistic biology fluency to reason step-by-step about pathways/drug mechanisms and what different assays can and cannot establish.
  • •Evidence judgment to assess whether data support a claim and to make that judgment reproducible via structure, schemas, or evaluation.
  • •Clear communication ability to explain methods, assumptions, results, and limitations to technical and interdisciplinary audiences.
Experience:Computational biologyComputational genomicsBioinformaticsMachine learningSingle-cell omicsPerturbation screensImaging-based proteomicsGenetics
Education:PhD / Doctorate in Computational Biology, Bioinformatics, Computational Genomics, Biostatistics, Machine Learning (or related quantitative field)
Skills:Scientific judgmentCommunicationCross-functional collaborationReproducible analysisData interpretation
Languages:English
Tech Stack:PythonVersion-controlled pipelines

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