Perceptive Space Systems
Machine Learning
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
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
Requirements
4+ 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.
To apply for this job please visit apply.workable.com.