Physics Informed Machine Learning Scientist

What you'll do

• Establish a scalable data management framework for legacy and new datasets from test benches and prototypes.
• Develop physics-informed machine learning models and scientific simulations to enable system-level tradeoff analysis.
• Integrate existing physics-based models into a master virtual model and establish infrastructure for deployment.
• Propose experimental anchoring studies and analyze test results to reduce model uncertainty.
• Provide input to technology roadmaps and identify key scientific learning objectives and de-risking activities.
• Troubleshoot code and algorithms for source operation, data streaming, storage, and queries.
• Document learnings and communicate technical knowledge to engineering and product development teams.

What we're looking for

• Ph.D. with at least 3 years of experience or a Master’s degree with at least 6 years of experience in an analytical field like mathematics, physics, or engineering.
• Extensive experience in physics-informed machine learning and model integration into scalable master models.
• Experience solving complex modeling problems using optimization and deep learning methodologies.
• Strong expertise in data management and building scalable data and training pipelines for end-to-end model development.
• Strong software development skills in Python and experience with deep learning frameworks like PyTorch or JAX.
• Proficiency in C/C++ (preferred).
• Proficiency in Matlab (preferred).
• Experience with database tools, automation frameworks, and experimental tracking platforms like MLflow.
• Experience with cloud and development environments such as Azure Kubernetes Service, Google Distributed Cloud Edge, or Apache Spark (preferred).
• Legal authorization to access controlled technology as defined in the United States Export Administration Regulations.