Scientist II, Mechanics & Extreme Materials

Lila Sciences•Cambridge, MA

About The Position

As a Scientist II, Mechanics & Extreme Materials on the Materials Science team at Lila Sciences, you will be the technical expert driving closed-loop learning for extreme materials. Your domain depth in coatings and metal alloys, durability testing, and microstructural analysis will directly shape the experimental methods and automated workflows that power Lila's autonomous science platform. This role sits at the center of Lila's extreme materials program focused on the predictive design of hard, wear-resistant, and corrosion-resistant coatings for aerospace, defense, and industrial applications. You will define how the platform interrogates the performance of new alloy and coating systems, what data it generates, and how those outputs feed back into the next experimental cycle. You will work alongside experimentalists, systems engineers, and machine learning scientists, providing the mechanical and metallurgical judgement that keeps closed-loop campaigns scientifically rigorous and accelerating toward next-generation coating and extreme material solutions.

Requirements

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 Haves

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).

Responsibilities

Work with Program Lead to drive the strategy for closed-loop campaigns on anti-wear and anti-corrosion materials.
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.
Collaborate with experimentalists, systems engineers, and ML scientists to integrate characterization outputs into autonomous closed-loop workflows and ML training datasets.
Lead experimental design and analysis to extract key materials properties and performance parameters for ML training.
Troubleshoot characterization workflows and instrumentation to sustain high-throughput operational performance.
Maintain accurate laboratory records and ensure compliance with safety and regulatory standards.