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Salary: USD 320,000 - 380,000 / annual
Lila Sciences is seeking a Director to lead strategy and execution for the Materials Science AISF (AI Science Factory) program. This role owns the program's charter, budget, timeline, and success criteria, translating product, science, and AI priorities into executable plans.
The Materials Science AISF program spans vacuum synthesis, characterization, electrochemistry, nanoporous materials, and physics. The Director will connect deep domain expertise with platform strategy and cross-functional execution to expand experimental systems that enable AI to learn materials science at scale.
Key responsibilities include:
- Defining and owning the Materials Science AISF program strategy, charter, budget, and roadmap
- Translating cross-functional priorities into clear execution plans
- Building roadmaps for expanding capabilities across synthesis, characterization, and closed-loop AI guidance
- Evaluating proof-of-concept results and designing pathways for platform integration
- Forming and aligning cross-functional project teams
- Managing resourcing, prioritization, and execution risks
- Representing program status and decisions in technical leadership reviews
The role reports to John Gregoire and partners closely with Product, AISF Engineering, Materials Experiment, Scientific Programs, and technical leadership.
Required qualifications:
- PhD or equivalent in materials science, condensed matter physics, chemistry, or related field with industry research experience
- Track record leading complex, multidisciplinary experimental programs
- Familiarity with Materials Science AISF domains (vacuum synthesis, characterization, electrochemistry, nanoporous systems)
- Experience defining and managing technical roadmaps with resource, budget, and timeline constraints
- Strong written and verbal communication skills
- Experience leading through influence across matrixed, cross-functional teams
Bonus experience includes automated/high-throughput experimental platforms, robotics, lab automation, AI/ML-driven experimental design, Bayesian optimization, atomistic simulation methods, and work in fast-paced research environments (national labs, advanced materials startups, deep-tech companies).
