This role is ideal for entrepreneurial engineers who want to build from first principles, move fast, and own core systems end-to-end and who take initiative, thrive in ambiguity, and be part of a demanding startup environment.

What You'll Do

• 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

Requirements

• 2+ years of industry experience following a Master’s or PhD in Physics, Aerospace, Electrical Engineering, Applied Math, or a related field
• Experience in fast-paced, high-ownership ML roles within a startup or a fast-moving, demanding startup-like environment.
• 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)
• Comfortable working with multidimensional datasets and integrating domain context into modeling
• Strong general foundations in software engineering, including coding standards, code reviews, source control (e.g., Git), build processes, and testing
• Experience deploying ML solutions onto cloud platforms (e.g., AWS, GCP, Azure)
• Track record of contributing to the successful delivery of production-ready ML models
• Able to explain model behavior, assumptions, and limitations clearly to both technical and non-technical stakeholders
• Excellent communication and collaboration skills; able to work effectively across disciplines

Bonus If You Have

• Experience working in early-stage start ups or cross-disciplinary R&D teams
• Experience working on scientific modeling, simulation data, or systems governed by physics or control principles
• Familiarity with techniques for uncertainty quantification and physics-informed ML
• A track record of publications or contributions to open-source ML libraries
• Proficient in C/C++ and Java

Additional Requirements

• The role is fully remote, BUT you are expected to be available during Eastern Time working hours.

Benefits

• Opportunity to work at the frontier of AI and aerospace, building first-of-its-kind products.
• Competitive stock option compensation
• Top-tier health and benefits coverage
• Fully remote team
• Opportunities to lead technical efforts as the team scales.