Senior/Principal ML Scientist, Translational Biology
About this role
Someone who thrives on untangling complex biological questions and enjoys translating messy human data into rigorous, scalable systems will find a natural home in this role. Lila Sciences operates at the intersection of automated data generation and scientific superintelligence, building loops where artificial intelligence and experimental biology co-evolve. Within this environment, the translational biology team focuses on assessing whether clinical programs possess robust mechanistic support before human testing.
The incoming Senior or Principal ML Scientist will build the computational infrastructure that evaluates therapeutic mechanisms against human genetic, expression, and trial-derived data. Instead of analyzing individual programs manually, this person creates the automated assessments, evaluation frameworks, and forecasting models that determine whether a proposed intervention actually operates in patients. Success requires deep scientific judgment, fluency in translational medicine, and the ability to codify expert evaluation into reproducible systems that scale across the organization.
What you will do
- Build automated systems that evaluate mechanistic support for clinical programs by turning structured biological evidence and models into scalable assessments.
- Analyze human genetic, expression, cohort, and trial-derived data to determine whether a mechanism is active and rate-limiting in target patient populations.
- Evaluate clinical trial designs for alignment with proposed mechanisms, including endpoint choice, biomarker strategy, and eligibility criteria.
- Develop outcome-verifiable forecasts of real program progression scored against historical information available at prediction dates.
- Collaborate closely with machine learning scientists, mechanism scientists, and engineers to translate model outputs into actionable decisions.
- Communicate research findings to technical and cross-functional stakeholders while supporting external scientific visibility through publications and presentations.
What you need
- Hold a PhD in a computational discipline such as translational bioinformatics, computational biology, biomedical informatics, biostatistics, epidemiology, or machine learning.
- Possess hands-on experience in machine learning and data analysis applied to translational medicine.
- Demonstrate fluent Python programming skills alongside substantial experience analyzing human genetic, multi-omic, cohort, or real-world data in reproducible pipelines.
- Apply mechanistic reasoning to therapeutic interventions by understanding how treatments work and evaluating supporting human evidence.
- Bring working knowledge of clinical development, including trial design, endpoints, biomarker strategies, and patient enrichment criteria.
Nice to have
- Experience with human genetics for target identification and validation, including common and rare variant evidence or QTL data.
- Experience with biomarker development, patient stratification, companion diagnostics, or enrichment strategy.
- Familiarity with survival analysis, competing risks, calibration, or forecasting methodology.
- Experience evaluating language models or agents on scientific judgment tasks with contamination and memorization controls.
- Experience with structured evidence frameworks such as GRADE or systematic review protocols.
Benefits
- Comprehensive medical, dental, and vision coverage
- Employer-paid life and disability insurance
- Flexible time off with generous company-wide holidays and paid parental leave
- Educational assistance program and commuter benefits with bike share memberships
- Company-subsidized lunch program
Skills and tech stack
Questions candidates ask
What is the compensation range for this role?
The expected base salary range for U.S.-based positions is $268,000 to $384,000 USD, with additional bonus potential and early-stage equity.
What kind of systems will I be building?
You will build systems that assess mechanistic support for clinical programs, ground those mechanisms in human data, and evaluate trial designs at scale.
What qualifications are required for the background?
Applicants need a PhD in a computational discipline with research centered on human biomedical data, alongside hands-on machine learning experience for translational medicine.
Will I have opportunities to publish research?
Yes, the team supports external scientific visibility through publications, presentations, and engagement with the broader computational biology and therapeutic discovery communities.