AI Scientist - Representation Learning for Materials Science
Skills
- Proficiency in Python and deep learning frameworks (PyTorch, JAX or similar)
- Experience with representation learning (self-supervised, unsupervised) and geometric/graph-based methods
- Familiarity with materials science, physics or chemistry principles and how to encode them in models
- Experience deploying scalable ML workflows and working cross-functionally with experimental teams
- Strong communication and presentation skills
Responsibilities
- Design and implement novel representation learning architectures for multi-modal materials data
- Develop self-supervised and unsupervised methods to learn meaningful embeddings from diverse materials data streams
- Incorporate physics-informed inductive biases and geometric deep learning into model design
- Collaborate with materials scientists and experimental teams to validate and iterate models on real lab data
- Deploy scalable end-to-end workflows and partner with engineers to productionize models
- Translate scientific questions into modeling strategies and present results to R&D and product stakeholders
Education
- MS or PhD in Computer Science, Materials Science, Physics, Chemistry or a related field preferred
- Demonstrated research or industry experience in machine learning or scientific computing
Benefits
- Competitive salary and equity package
- Comprehensive health, dental, and vision insurance
- Flexible/hybrid work arrangements and paid time off
- Professional development and conference support
- 401(k) or retirement plan options
- Collaborative, mission-driven startup environment
Average salary estimate
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