ML Scientist - Materials Performance Modeling
Skills
- Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, JAX).
- Experience with modeling sparse, time-dependent, or few-shot datasets.
- Familiarity with experimental and/or computational materials datasets and performance characterization.
- Knowledge of physics-informed ML, probabilistic modeling, and multi-modal data integration.
- Strong collaboration and communication skills for cross-disciplinary work.
Responsibilities
- Develop ML models to predict materials performance and reliability across conditions such as stress, temperature, chemical environments, and aging.
- Design data-efficient learning strategies for sparse, small, or incomplete experimental datasets.
- Integrate physics-informed priors, probabilistic modeling, time-series prediction, and multi-modal methods into predictive frameworks.
- Collaborate with materials scientists to curate, preprocess, and interpret experimental and simulation data.
- Build scalable ML workflows and pipelines for deployment within Lila’s scientific platform.
Education
- PhD in Materials Science, Applied Physics, Machine Learning, Computer Science or related field preferred.
- Advanced degrees or equivalent industry/research experience will be considered.
Benefits
- Equity ownership / stock options
- Health, dental, and vision insurance
- Flexible PTO and paid time off
- 401(k) or retirement savings plan options (where applicable)
- Collaborative, mission-driven research environment with professional development support
Average salary estimate
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