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Lila Sciences is building an agentic drug discovery platform that uses AI agents to accelerate molecular design and optimization. This role sits at the intersection of computational biophysics, molecular simulation, and AI-driven scientific workflows.
You will define how biophysics experiments should be set up, parameterized, validated, and exposed as trustworthy tools for automated scientific discovery. This is a hands-on scientific role focused on ensuring that AI agents can use biophysics tools in ways that are scientifically correct, reliable, and useful for drug discovery.
Key responsibilities include:
- Define scientific requirements for agent-usable computational biophysics tools, including inputs, assumptions, and decision-grade outputs
- Design and validate molecular simulation workflows for drug discovery, including MM/GBSA and free-energy perturbation methods (RBFE/ABFE)
- Establish standards for system setup, force field selection, parameterization, equilibration, sampling, analysis, and quality control
- Build robust, validated RBFE and ABFE workflows with automated checks for chemical-series compatibility and ligand-pose consistency
- Partner with research engineers to turn biophysics protocols into reliable APIs and guardrails that LLM agents can invoke correctly
- Build validation benchmarks and acceptance criteria for molecular dynamics and binding free-energy workflows
- Evaluate and integrate open-source tools (OpenMM, OpenFE, etc.)
- Identify and mitigate failure modes in simulation setup, parameterization, sampling, and analysis
- Advise teams on when biophysics workflows are appropriate and where limitations matter
- Partner with ML teams to refine methods as new experimental data becomes available
- Scale validated workflows across large GPU fleets with reproducibility and traceability
Required qualifications:
- PhD or equivalent in computational biophysics, computational chemistry, chemical physics, biophysics, physics, chemistry, or related field
- Deep hands-on experience with molecular dynamics simulation, MM/GBSA, and free-energy perturbation methods
- Practical experience with open-source molecular simulation packages, especially OpenMM and OpenFE
- Strong understanding of molecular parameterization and validation of simulation setups
- Demonstrated history of modeling protein-ligand binding for scientific decision-making
- Ability to reason from biophysical first principles while building practical workflows
- Experience translating complex scientific methods into tooling requirements and operational workflows
- Strong cross-functional communication skills
Bonus experience includes industry drug discovery background, GPU simulation infrastructure, automated scientific workflows, cofolding/structure prediction, benchmark suite design, perturbation mapping, machine-learned potentials, and familiarity with LLM agents.
