Principal Scientist / Associate Director, Agentic AI Research for Materials Science

Lila Sciences
Cambridge, San Francisco
Workplace: OnsiteFull timeUSD 288,000 - 420,000 annuallyFunction: Research & Scientific (R&D)Experience: 5+ yearsEducation: phdSkills: ["Communication","Technical leadership","Cross-functional collaboration","Research-to-production translation","Scientific rigor"]

Lead the technical roadmap for agentic AI systems that accelerate materials science through autonomous planning, execution, and interpretation of experiments. Own end-to-end agent architecture using retrieval-augmented generation and multi-modal understanding, while driving scientific rigor and engineering quality as a player-coach. Hire, mentor, and grow a small cross-functional team, partner with experimental-infrastructure teams, and translate state-of-the-art research into shippable systems and publications.

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Lila Sciences
Lila Sciences
1 month ago

Principal Scientist / Associate Director, Agentic AI Research for Materials Science

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Last checked: 4 hours agoStatus: Live

Job Summary

Lead the technical roadmap for agentic AI systems that accelerate materials science through autonomous planning, execution, and interpretation of experiments. Own end-to-end agent architecture using retrieval-augmented generation and multi-modal understanding, while driving scientific rigor and engineering quality as a player-coach. Hire, mentor, and grow a small cross-functional team, partner with experimental-infrastructure teams, and translate state-of-the-art research into shippable systems and publications.
Location: Cambridge, San Francisco
Workplace: Onsite
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Director level

Key Responsibilities

  • •Define and execute the agentic AI roadmap for materials science, including agentic frameworks and retrieval-augmented generation.
  • •Lead agent system architecture and deliver end-to-end systems on real-world projects.
  • •Hire, mentor, and grow a small cross-functional team while setting standards for scientific rigor, code quality, and reproducibility.
  • •Partner with diverse teams to integrate agent systems with experimental infrastructure and deliver on real programs.
  • •Track state-of-the-art in agentic AI and scientific ML, translating external advances into internal direction and publishing when warranted.

Pay and Benefits

Salary: USD 288,000 - 420,000 annually
Perks:Health InsuranceDentalVisionLife InsuranceDisability InsurancePaid ParentalLearning BudgetCommuter BenefitsMeal AllowanceFlexible Time

Key Requirements

  • •PhD in Computer Science, Machine Learning, Materials Science, Chemistry, Physics, or a related field, with 5+ years of post-PhD research and applied ML experience.
  • •Proven experience building and shipping agentic systems, ML pipelines, or autonomous research workflows with measurable scientific or product impact.
  • •Deep expertise in modern ML, NLP, and reasoning (LLMs, agentic frameworks, tool use, planning, data extraction, and multi-modal data).
  • •Knowledge of materials science, computational chemistry, or condensed-matter physics to ground agent behavior in real constraints.
  • •Proficiency in Python and the ML software stack with strong engineering habits around reproducibility, testing, and production deployment.
Experience:5+ years
Education:PhD / Doctorate
Skills:CommunicationTechnical leadershipCross-functional collaborationResearch-to-production translationScientific rigor
Languages:English
Tech Stack:PythonLLMsNLPAgentic frameworksRetrieval-augmented generationMulti-modal dataTool usePlanningData extractionWorkflow orchestrationDistributed computeCloudHPC

Company Brief

Lila Sciences
Develops AI-driven platforms to accelerate drug discovery and biological research by integrating machine learning with chemical and biological data to predict molecular properties, streamline candidate selection, and enable faster therapeutic development.
Industry: Biotech
Company Size: Small (11 to 50 employees)
Growth: Early Stage Startup
Funding: Seed
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