Graduate/PhD Research Intern, Machine Learning
Resumen del puesto por JobGrid
Graduate/PhD Research Intern, Machine Learning at Constellation Space (YC W26): Seattle, Estados Unidos; Presencial; Prácticas; Tecnología; Data Science y ML. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: Seattle, Estados Unidos, Presencial
- Role classification: Tecnología, Data Science y ML, Prácticas
- Source freshness: checked by JobGrid on 2026-05-28.
- Application path: candidates continue to the employer application page with non-personal referral tags.
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