Scientist Ii, Mechanics & Extreme Materials

Lila Sciences Lila Sciences · AI Frontier · Alewife, Cambridge, MA · Autonomous Science Platform

Scientist II role focused on driving closed-loop learning for extreme materials (coatings, metal alloys) within an autonomous science platform. Responsibilities include defining experimental methods, integrating characterization outputs into ML training datasets, and leading experimental design. Requires PhD in a relevant field, expertise in mechanical and microstructural characterization, and a track record in developing performance-driven materials. Bonus points for experience with autonomous workflows and ML-guided design.

What you'd actually do

  1. Work with Program Lead to drive the strategy for closed-loop campaigns on anti-wear and anti-corrosion materials.
  2. Develop, execute and optimize characterization and testing workflows for extreme environment materials, including coatings and bulk materials. Focus areas include: microstructural, mechanical, and tribological analysis for process-structure-property relationships.
  3. Collaborate with experimentalists, systems engineers, and ML scientists to integrate characterization outputs into autonomous closed-loop workflows and ML training datasets.
  4. Lead experimental design and analysis to extract key materials properties and performance parameters for ML training.
  5. Troubleshoot characterization workflows and instrumentation to sustain high-throughput operational performance.

Skills

Required

  • PhD in Materials Science, Metallurgy, Mechanical Engineering, or related field with 3+ years post-PhD or industry experience.
  • Proficiency in nanoindentation-based mechanical characterization of thin films and bulk materials, including hardness, wear rate, elastic modulus, tribology, and high-throughput testing protocol development.
  • Solid mechanics foundation and deep expertise in microstructural characterization of metal alloys: SEM, TEM, EBSD.
  • Track record developing anti-wear or anti-corrosion materials with measurable performance outcomes.
  • Strong grounding in physical metallurgy and ceramics science: phase equilibria, transformations, sintering, deformation mechanisms, and degradation in extreme environments.
  • Proven ability to interpret structure-property relationships and translate them into experimental design decisions.
  • Effective scientific communicator with experience collaborating across experimental, engineering, and computational teams.

Nice to have

  • Experience with high-throughput or autonomous experimental workflows.
  • Proficiency in Python or similar tools for data analysis and workflow automation.
  • Exposure to coating deposition and bulk alloy synthesis techniques.
  • Experience with ML-guided experimental design or active learning frameworks.
  • Familiarity with CALPHAD modeling (Thermo-Calc, Pandat) for alloy or ceramic phase prediction.
  • Background in extreme-environment materials testing (high temperature, high stress, or corrosive).

What the JD emphasized

  • Track record developing anti-wear or anti-corrosion materials with measurable performance outcomes.