We are looking for a PhD-level Research Scientist to build the models behind ConstellationOS. You will develop forecasting and decision models for satellite link conditions and ground-network operations, and prove them against real data and physics-based baselines. Your work shows up in customer pilots and government evaluations.
ResponsibilitiesDevelop and train machine learning models that forecast link conditions and support operational decisions.
Own data acquisition and preprocessing for telemetry, weather, atmospheric, and RF datasets.
Benchmark learned models against deterministic physics models and report results honestly, including failures.
Do simulation research for GNC and RF systems.
Work with academic groups and publish or present results
Turn research into code the platform team can ship.
Ph.D. (or near completion) in Machine Learning, Applied Mathematics, Physics, Aerospace, Electrical Engineering, or a related field.
Publications or equivalent research output in forecasting, deep learning, reinforcement learning, or scientific ML.
Strong Python and PyTorch or JAX. Able to write research code others can run.
Experience with time-series, signal, or physical-system data is a plus.
U.S. person required (ITAR).
Skills Required
- B.S. or M.S. in Computer Science, Engineering, or equivalent experience.
- Proven experience deploying machine learning models into production.
- Strong software engineering skills in Python and C++.
- Experience with cloud platforms, containerization (Docker), and MLOps tools.
What We Do
Constellation Space builds ConstellationOS, an AI-native operating system that forecasts satellite link degradation minutes ahead and autonomously reroutes traffic under policy to prevent data loss. The platform ingests telemetry, uses physics-informed ML to predict failures with high accuracy, and executes rapid, policy-bound reroutes to provide mission assurance and scalable orchestration for large satellite constellations.
