ML Scientist - Materials Performance Modeling
Skills
- Strong Python proficiency and software engineering best practices.
- Experience with modern ML frameworks such as PyTorch, TensorFlow, or JAX.
- Proven work on time-dependent / sequential data and few-shot or data-efficient learning.
- Familiarity with experimental and/or computational materials datasets and performance characterization.
- Experience integrating physics-informed or hybrid physics/ML approaches into models.
- Ability to communicate results and collaborate across ML and materials science teams.
Responsibilities
- Develop predictive ML models for materials performance and reliability under varied application conditions (stress, temperature, chemical exposure, aging).
- Design and implement data-efficient learning strategies for sparse, small, or incomplete experimental datasets.
- Incorporate physics-informed priors, probabilistic modeling, time-series approaches, and multimodal methods into modeling frameworks.
- Collaborate with materials scientists to curate, preprocess, and interpret experimental and simulation datasets.
- Build scalable ML workflows and pipelines for integration and deployment in Lila’s autonomous platforms.
- Validate and benchmark models against experimental results, and iterate with domain scientists to refine modeling assumptions.
Education
- PhD preferred in Materials Science, Applied Physics, Machine Learning, Computer Science, or related field (equivalent industry experience considered).
- Strong background in quantitative modeling and statistical methods.
Benefits
- Competitive salary and equity participation (stock options)
- Medical, dental, and vision insurance
- 401(k) or retirement plan access (where applicable)
- Flexible PTO and paid time off policies
- Professional development and conference support
- Collaborative, interdisciplinary team and access to autonomous lab infrastructure
Average salary estimate
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