Scientist II/Senior Scientist, Computational Chemistry, Drug Discovery
Lila Sciences · CA, US
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About the Role
Lila Sciences is seeking a Scientist, Computational Chemistry, Drug Discovery to guide, evaluate, and improve AI-driven drug discovery workflows. This role involves ensuring AI-generated plans and prioritizations are scientifically sound and contributing directly to drug discovery programs when computational chemistry leadership is needed. The position focuses on making agent-guided discovery scientifically useful and spans various computational chemistry tasks, including the design and supervision of tools for agentic workflows.
Responsibilities
- Monitor and review drug discovery agents' computational chemistry workflows, recommendations, and optimization plans for scientific and chemical validity.
- Evaluate agent-generated drug discovery plans for scientific coherence.
- Advise on compound prioritization across discovery programs, considering various tradeoffs.
- Define which computational chemistry tools agents should use, their inputs, and output interpretation.
- Lead computational chemistry strategy for live drug discovery programs when needed.
- Build, adapt, or guide the creation of open-source-first workflows for various computational chemistry tasks.
- Apply protein-ligand binding modeling to support hypothesis generation, compound design, and prioritization.
- Partner with cross-functional teams to improve AI-assisted discovery loops.
- Evaluate agent-generated molecular design ideas and identify weaknesses.
- 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.
Requirements
- 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, developability, 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, MDAnalysis, or comparable tools.
- Ability to evaluate computational recommendations critically and communicate uncertainty, assumptions, and limitations clearly.
- Comfort working alongside AI systems, including reviewing, guiding, and improving agent-generated plans.
- Strong collaboration skills across chemistry, biology, ML, computational science, and engineering teams.
Skills
- Computational Chemistry
- Drug Discovery
- AI-driven workflows
- Compound prioritization
- Modeling workflows
- Docking
- Virtual screening
- SAR modeling
- Molecular property prediction
- Medicinal chemistry support
- Live program support
- Python
- RDKit
- Biopython
- OpenMM
- MDAnalysis
- Protein-ligand binding modeling
- Cheminformatics
- FEP
- MM/GBSA
- Molecular dynamics
Location
- U.S.
Work Type
- Full-time
Experience Level
- PhD or equivalent experience
- Strong practical experience
- Demonstrated history
- Working knowledge
- Strong medicinal chemistry experience
- Hands-on experience
- Comfort working alongside AI systems
- Strong collaboration skills
- Industry drug discovery experience (bonus)
- Experience extending, integrating, or contributing to open-source scientific software (bonus)
- Experience with FEP, MM/GBSA, molecular dynamics, or other physics-based scoring workflows (bonus)
- Experience integrating computational chemistry workflows into automated or agentic systems (bonus)
- Exposure to DEL, high-throughput screening, or other large experimental datasets (bonus)
- Experience with prospective compound prioritization in active discovery programs (bonus)
Education Level
- PhD or equivalent experience in computational chemistry, chemistry, cheminformatics, molecular modeling, biophysics, or a related field.
Salary/Compensations
- $140,800—$217,800 USD
Benefits
- Competitive base compensation with bonus potential
- 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 (International)
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.
- 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.