Graduate/PhD Research Intern, Machine Learning

About Constellation

Constellation is building software to model, predict, and improve satellite network operations. We combine simulation, data/ML workflows, and product-facing platform systems to support better operational decisions.

Role Overview

We are seeking a research-focused ML intern (MS/PhD level) to help advance our modeling and experimentation capabilities. This role is ideal for someone who enjoys turning research ideas into rigorous experiments and high-quality prototypes that can influence real product and platform direction.

Note: This role requires access to ITAR-controlled data, so we need candidates to be U.S. persons (citizen, green card holder, or asylee/refugee).

What You’ll Do

- Design and run ML experiments for forecasting and anomaly/risk prediction in network operations

- Develop and evaluate models using time-series, probabilistic, and simulation-informed approaches

- Improve feature engineering, dataset quality, and evaluation methodology

- Build reproducible research workflows for training, validation, and model comparison

- Communicate findings through clear technical writeups and recommendations

What We’re Looking For

- Currently enrolled in an MS or PhD program (CS, EE, Aerospace, Applied Math, or related)

- Strong low level engineering skills and comfort with scientific/ML tooling (C++, Python, Rust)

- Ability to own projects end-to-end: scoping, implementation, testing, and communication

- Clear written/verbal communication and strong collaboration habits

Nice to Have

- Experience with APIs, cloud infrastructure, or data-intensive systems

- Familiarity with model evaluation, experiment tracking, and reproducibility

- Background in networking, geospatial systems, telecom, or space-tech