Physics Informed Machine Learning Scientist

What you'll do

• Establish a scalable data management framework to ensure quality and readiness for machine learning workflows.
• Develop physics-informed machine learning models and simulations to enable system-level tradeoff analysis.
• Integrate existing physics-based models into a master virtual model and establish infrastructure for maintenance.
• Propose experimental anchoring studies to reduce model uncertainty and extract actionable knowledge from data.
• Provide input on technology roadmaps by identifying de-risking activities and key scientific learning objectives.
• Troubleshoot code and algorithms related to source operation, data streaming, storage, and queries.
• Document findings and communicate technical knowledge to engineering teams to guide product improvements.

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 math, physics, or engineering.
• Extensive experience in physics-informed machine learning and integrating models into scalable master models.
• Experience solving complex modeling problems using optimization and deep learning methodologies.
• Expertise in data management and building scalable data and training pipelines for end-to-end model development.
• Strong software development skills in Python with experience in deep learning frameworks like PyTorch or JAX.
• Proficiency in C/C++ and Matlab is preferred.
• Experience with database tools, automation frameworks, and experimental tracking platforms such as MLflow.
• Must be legally authorized to access controlled technology under U.S. Export Administration Regulations.