Scientist II​/Senior Scientist, Computational Chemistry, Drug Discovery

Scientist II/Senior Scientist, Computational Chemistry, Drug Discovery

Lila Sciences is seeking a Scientist, Computational Chemistry, Drug Discovery to help guide, evaluate, and improve AI-driven drug discovery workflows. This person will bring practical computational chemistry experience in a drug discovery context and will work alongside AI systems to make sure agent-generated optimization plans, compound prioritizations, and modeling workflows are chemically and scientifically sensible. The primary mandate is to make agent-guided discovery scientifically useful, while also contributing directly to live drug discovery programs when human computational chemistry leadership is needed.

This is a role for someone who can look at an agent-driven drug optimization rollout and answer hard questions:
Does this plan make sense? Are the right compounds being prioritized? Are the right modeling tools being used? What additional tools, constraints, or review steps should be built so agents can make better discovery decisions? When needed, this person can also step into an active discovery effort and lead the computational chemistry strategy for pursuing a drug program. The role spans docking, virtual screening, SAR modeling, molecular property prediction, compound prioritization, medicinal chemistry support, live program support, and, most centrally, the design and supervision of computational chemistry tools for agentic workflows.

Monitor and review drug discovery agents' computational chemistry workflows, recommendations, and optimization plans for scientific and chemical validity.

Evaluate agent-generated drug discovery plans that combine chemistry, biophysics, cofolding, simulation, assay, and low-data model outputs, and determine whether the resulting optimization strategy is scientifically coherent.

Advise on compound prioritization across discovery programs, including tradeoffs between potency, selectivity, develop ability, uncertainty, and experimental feasibility.

Define which computational chemistry tools agents should use, when they should use them, what inputs are required, and how outputs should be interpreted.

Lead computational chemistry strategy for live drug discovery programs when needed, including hypothesis generation, modeling plans, compound prioritization, and interpretation of results.

Build, adapt, or guide the creation of open-source-first workflows for docking, virtual screening, SAR analysis, conformer generation, pharmacophore modeling, QSAR, ADMET and property modeling, and cheminformatics.

Apply protein-ligand binding modeling to support hypothesis generation, compound design, and prioritization.

Partner with medicinal chemists, biologists, computational biophysicists, cofolding and low-data ML scientists, and research engineers to improve AI-assisted discovery loops.

Evaluate agent-generated molecular design ideas and identify when proposed chemistry, binding hypotheses, or optimization strategies are weak or unsupported.

Help establish validation standards, review protocols, and guardrails for computational chemistry tools used by AI systems.

Translate computational chemistry judgment into practical requirements for agent tools, workflows, benchmarks, and decision criteria.

PhD or equivalent experience in computational chemistry, chemistry, cheminformatics, molecular modeling, biophysics, or a related field.

Strong practical experience applying computational chemistry in a drug discovery context, including active program support or leadership.

Demonstrated history of modeling protein-ligand binding and using those models to inform discovery decisions.

Working knowledge across docking, virtual screening, SAR modeling, conformer generation, pharmacophore modeling, QSAR, ADMET or property prediction, and cheminformatics.

Strong medicinal chemistry experience and the ability to reason about compound optimization, SAR, develop ability, and synthetic or experimental tradeoffs.

Fluency in Python and hands-on experience building open-source computational chemistry workflows with libraries such as RDKit, Biopython, OpenMM, MD Analysis, or comparable tools.

Ability to evaluate computational…