Graduate/PhD Research Intern, Machine Learning

<p><strong>About Constellation</strong></p>
<p>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.</p>
<p><strong>Role Overview</strong></p>
<p>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.</p>
<p><strong>What You’ll Do</strong></p>
<p><em>- Design and run ML experiments for forecasting and anomaly/risk prediction in network operations</em></p>
<p><em>- Develop and evaluate models using time-series, probabilistic, and simulation-informed approaches</em></p>
<p><em>- Improve feature engineering, dataset quality, and evaluation methodology</em></p>
<p><em>- Build reproducible research workflows for training, validation, and model comparison</em></p>
<p><em>- Communicate findings through clear technical writeups and recommendations</em></p>
<p><strong>What We’re Looking For</strong></p>
<p><em>- Currently enrolled in an MS or PhD program (CS, EE, Aerospace, Applied Math, or related)</em></p>
<p><em>- Strong low level engineering skills and comfort with scientific/ML tooling (C++, Python, Rust)</em></p>
<p><em>- Ability to own projects end-to-end: scoping, implementation, testing, and communication</em></p>
<p><em>- Clear written/verbal communication and strong collaboration habits</em></p>
<p><strong>Nice to Have</strong></p>
<p><em>- Experience with APIs, cloud infrastructure, or data-intensive systems</em></p>
<p><em>- Familiarity with model evaluation, experiment tracking, and reproducibility</em></p>
<p><em>- Background in networking, geospatial systems, telecom, or space-tech</em></p>