Scientist II/Senior Scientist, Computational Biophysics

Your Impact at LILA

Lila Sciences is seeking a Computational Biophysics Scientist to help ensure that our AI agents can use biophysics tools in ways that are scientifically correct, reliable, and useful for drug discovery. This person will define how biophysics experiments should be set up, parameterized, validated, and exposed as tools for automated scientific workflows.

The role sits at the intersection of computational biophysics, molecular simulation, and agentic platform design. You will work with computational scientists, research engineers, ML scientists, and drug discovery teams to build a scalable biophysics platform capable of running across hundreds of GPUs while preserving scientific rigor. This is a hands-on scientific role: the expectation is not heavy production software engineering, but strong ownership of the scientific logic, validation standards, and tool requirements that make automated biophysics trustworthy.

This role is differentiated from the broader computational chemistry position by its depth in statistical mechanics, molecular simulation, and the theory, setup, and validation of relative and absolute binding free-energy methods.

This role is one part of a broader agentic drug discovery system: computational chemistry helps judge whether agent-generated optimization strategies are chemically sensible, ML scientists build cofolding and low-data learning models, and research engineers help turn validated scientific protocols into reliable scalable infrastructure.

What You'll Be Building

• Define the scientific requirements for agent-usable computational biophysics tools, including what inputs are required, what assumptions are acceptable, and what outputs are decision-grade.
• Design and validate molecular simulation workflows for drug discovery applications, including MM/GBSA and free-energy perturbation methods for relative and absolute binding free energies (RBFE/ABFE).
• Establish standards for system setup, force field selection, molecular parameterization, equilibration, sampling, analysis, and quality control.
• Build robust, validated RBFE and ABFE workflows with automated checks for chemical-series compatibility, ligand-pose consistency, and other conditions required for trustworthy agent use.
• Work with research engineers to turn biophysics protocols into reliable tools, APIs, and guardrails that LLM agents can invoke correctly.
• Build validation benchmarks and acceptance criteria for molecular dynamics, binding free-energy, and related computational workflows.
• Evaluate and integrate open-source molecular simulation and free-energy tools, especially OpenMM, OpenFE, and adjacent packages.
• Identify and mitigate failure modes in simulation setup, parameterization, sampling, analysis, and interpretation.
• Advise teams on when a biophysics workflow is appropriate, what evidence it can support, and where its limitations matter.
• Partner with ML teams to refine molecular simulation methods and parameters as new experimental data becomes available.
• Partner with research engineering and infrastructure teams to scale validated workflows across large GPU fleets with reproducibility and traceability.

What You'll Need to Succeed

• PhD or equivalent experience in computational biophysics, computational chemistry, chemical physics, biophysics, physics, chemistry, or a related field.
• Deep hands-on experience with molecular dynamics simulation, MM/GBSA, and free-energy perturbation methods for RBFE and ABFE.
• Practical experience with open-source molecular simulation and free-energy packages, with strong preference for OpenMM and OpenFE.
• Strong understanding of how to parameterize molecular systems and validate that simulation setups are scientifically sound.
• Demonstrated history of modeling protein-ligand binding and interpreting results for scientific decision-making.
• Ability to reason from biophysical first principles while also building practical workflows that other teams can use.
• Experience translating complex scientific methods into tooling requirements, review standards, and operational workflows.
• Strong cross-functional communication skills and comfort working with computational scientists, ML researchers, research engineers, and drug discovery teams.

Bonus Points For

• Industry drug discovery experience, especially in structure-based or computational discovery programs.
• Familiarity with large-scale GPU simulation infrastructure or distributed scientific computing.
• Experience building automated or semi-automated scientific workflows.
• Familiarity with cofolding, structure prediction, or ML-assisted molecular modeling methods.
• Experience defining benchmark suites, regression tests, or validation harnesses for scientific software.
• Experience designing perturbation maps for robust RBFE statistics.
• Experience integrating machine-learned interatomic potentials or hybrid force-field simulations.
• Experience with active learning or fine-tuning of simulation parameters against experimental data for defined chemical or biological systems.
• Comfort working with LLM agents or agentic workflow systems.