Senior Machine Learning Engineer - Space Weather and Operational Resilience
Remote
AI Summary
Perceptive Space develops a decision intelligence platform to enhance satellite and launch vehicle safety amid space environment risks. The role involves building ML models for time series and spatio-temporal dynamics, integrating domain knowledge, and deploying solutions in a fast-paced startup setting. The position offers opportunities to work at the intersection of aerospace, AI, and real-time systems, with a focus on innovation and impact.
Key Highlights
Build and evaluate ML models for forecasting and space environment dynamics
Collaborate with aerospace and software teams to deploy ML systems
Requires experience in startup environments, cloud deployment, and modeling temporal data
Technical Skills Required
Benefits & Perks
Remote work with Eastern Time availability
Stock option compensation
Health insurance and benefits
Job Description
Perceptive Space Systems is building a decision intelligence platform to help satellite and launch operators navigate the growing risks posed by space weather and the space environment. We work at the intersection of aerospace, AI, and real-time systems, combining cutting-edge modeling, sensor fusion, and autonomy to improve operational resilience in orbit. Read more here.
Join us at the frontier of space technology and AI
You will build the foundational technology required for satellites, launch vehicles, and human missions to operate safely and efficiently in the harsh space environment
As part of our small, high-velocity team, you'll work at the intersection of aerospace, autonomy, and applied AI, solving real-world challenges with immediate mission impact.
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
- 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
- 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
- The role is fully remote, BUT you are expected to be available during Eastern Time working hours.
- 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