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Graduate/PhD Research Intern, Machine Learning

Constellation Space - Seattle | OnSite

Posted Jun 10, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
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  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
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Verification
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Salary
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401(k) match
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Market context

U.S. role benchmark (BLS OEWS)
$111,944 U.S. median for this role
Projected growth (BLS Employment Projections)
+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function
Data From the posting source checked Jun 20, 2026
Seniority
Entry From the posting source checked Jun 20, 2026
Work mode
Onsite From the posting source checked Jun 20, 2026
In-office days
5 days From the posting source checked Jun 20, 2026

Schedule

Shift type
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Weekend work
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Application

Cover letter
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Assessment
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Deadline
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Where they hire

State eligibility is not yet verified.

About this role

Graduate/PhD Research Intern, Machine Learning Seattle | OnSite 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

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