Machine Learning Scientist

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Requirements:

• 4+ years of industry experience following a Master’s or PhD in Physics, Electrical Engineering, Applied Math, or a related field
• Proficient in Python and experienced with deep learning frameworks such as PyTorch or TensorFlow
• Experienced with tools and frameworks like MLflow, Ray, Dask, and Numba
• Strong background in modeling temporal or sequential data (e.g., time series forecasting, state-space models, signal processing)
• Experience deploying ML solutions onto cloud platforms (e.g., AWS, GCP, Azure)

Responsibilities:

• Build and evaluate machine learning models for time series forecasting and spatio-temporal dynamics
• Design experiments to assess model generalization, uncertainty, and relevance to physical systems
• Integrate domain knowledge, external signals, or prior constraints to improve model performance
• Optimize model performance through feature engineering, architecture tuning, and validation strategies
• Collaborate with aerospace engineers, software engineers, and domain experts to deploy ML systems in production
• Stay up to date with developments in ML for dynamic systems, forecasting, and scientific ML