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About the Role
Lila Sciences is redefining the future of medicine by combining automated large-scale data generation with scientific superintelligence. The Life Science AI team develops machine learning systems for automated reasoning on biological data. This role focuses on domain models for perturbation biology, genetics, and high-dimensional experimental readouts that connect biological mechanism, experimental intervention, and therapeutic opportunity.
Responsibilities
- Build domain models for perturbation, genetic, and multimodal experimental data, and translate biological questions into rigorous ML problem formulations.
- Build models with interpretable structure where biology supports it, grounding representations in the machinery that executes a biological process rather than in cell-type identity embeddings.
- Build models with parameters a biologist can argue with.
- Make uncertainty a deliverable through calibrated posteriors, honest error bars, and outputs that inform downstream consumers about prediction reliability.
- Pre-register and beat baselines, including simple entity-mean, additive, and linear baselines.
- Partner with experimental scientists to guide data generation and model validation, shaping what gets measured, in what contexts, and at what precision.
- Build experiment-selection methods to choose the next batch of measurements that reduce uncertainty where it matters.
- Design benchmarks that connect model performance to biological and therapeutic consequence.
- Apply model outputs to prioritize targets and mechanisms.
- Contribute technical direction to high-impact modeling programs.
- Support the integration of domain models into agentic workflows.
- Deploy tools and help engineer agent harnesses that incorporate these tools.
- Collaborate on creating environments to train the reasoning models that drive agents.
Requirements
- PhD in machine learning, statistics, computational biology, computer science, bioengineering, physics, or a related quantitative field, with a strong publication record or equivalent industry impact.
- Experience developing models for high-dimensional biological data, and the judgment to connect ML methods to biological mechanism and experimental design.
- Track record of building models that predict into conditions not directly observed (sparse or unbalanced experimental designs, held-out combinations, transfer to new contexts) rather than interpolating within a densely sampled corpus.
- Comfort with calibration, proper scoring rules, and evaluation design, in work where decisions were based on your numbers.
- Strong programming skills and reliable ML research workflows.
- Clear communication across ML, biology, and experimental teams.
- Track record of leading ambiguous research problems from formulation through execution.
- 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 ML/AI for Biology, computational biology, and therapeutic discovery communities.
Skills
- Mechanistic and probabilistic modeling
- Bayesian hierarchical models
- Simulation-based or likelihood-free inference
- Amortized posterior inference
- State-space or ODE-based models
- Neural differential equations
- Mechanism-informed ML
- Pharmacokinetic/pharmacodynamic modeling
- Systems-biology modeling
- Physical modeling
- Active learning
- Bayesian optimal experimental design
- Closed-loop experimentation
- Lab-in-the-loop systems
- Deploying research models into scientific decision-making workflows
- Serving models as tools other systems call
Location
- U.S.
Work Type
- Full-time
Experience Level
- Senior
Education Level
- PhD
Salary/Compensations
- $268,000—$358,000 USD
Benefits
- Competitive base compensation with bonus potential and generous early-stage equity.
- Medical, dental, and vision coverage.
- Employer-paid life and disability insurance.
- Flexible time off with generous company wide holidays.
- Paid parental leave.
- Educational assistance program.
- Commuter benefits, including bike share memberships for office based employees.
- Company subsidized lunch program.
- Comprehensive benefits program tailored to their region (for international employees).
About the Company
- Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges.
- We believe science is the most inspiring frontier for AI.
- Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
- LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy.
- Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance.
Equal Opportunity
- Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.