Space ML Engineer - Onsite Westminster, CO

The Judge Group; Westminster (CO); On-site; USD 90,000 - 140,000; Full time

Job summary

The Judge Group is seeking a Data Science Engineer to develop ML components and the integration infrastructure that support space-domain awareness analytics pipelines. On-site in Westminster, CO you will build trajectory-classification and anomaly-detection models that operate on orbit-determination output.

You will also build benchmarking and evaluation frameworks to ensure pipeline performance under sparse, noisy observations while collaborating with astrodynamics and embedded-systems teams to

Qualifications

• BS or MS in Computer Science, Applied Mathematics, Statistics, Aerospace Engineering, Physics, or a related quantitative field.
• 4+ years of applied machine learning experience (or equivalent).
• Strong proficiency in Python and the scientific stack (NumPy, SciPy, pandas).
• Fluency in at least one deep-learning framework (PyTorch is strongly preferred).
• Demonstrated experience building ML systems on time-series, sequential, or state-estimation-adjacent data, rather than just tabular or vision benchmarks.
• Sound understanding of evaluation methodology, including class imbalance, calibration, uncertainty quantification, and the failure modes of small or synthetically generated datasets.
• Strong software-engineering discipline sufficient for a shared codebase, including version control, testing, reproducible environments, and documented interfaces.
• Clear technical writing skills for producing high-quality customer-facing deliverables.
• Must be able to obtain and hold a U.S. security clearance.

Responsibilities

• Develop AI/ML models for trajectory classification across various orbit regimes and families, and for the detection of anomalous dynamical behavior.
• Integrate and maintain end-to-end analytical pipelines spanning observation processing, hypothesis generation, orbit estimation, propagation, and classification.
• Define system interfaces and take full ownership of a shared, reproducible codebase.
• Build benchmarking and evaluation frameworks to measure estimator convergence behavior, classification accuracy and confusion structure, false-positive/negative characterization, time-to-custody, and sensitivity to track gaps and elevated measurement uncertainty.
• Design experiments that distinguish genuine model generalization from dataset artifacts—including held‑out families, degraded-observation ablations, and cross‑checks against independent reference datasets.
• Produce calibrated confidence metrics suitable for downstream operational use, documented precisely enough to support critical operator decisions.
• Partner with embedded‑systems staff to characterize model complexity, memory footprint, and inference latency; identify quantization, pruning, or architectural simplifications that meet deployment constraints.
• Contribute machine-learning expertise to CONOPS and systems‑engineering activities, including data‑flow definition, model lifecycle and retraining considerations, and the identification of critical technology elements.

Skills

Python

NumPy

SciPy

pandas

PyTorch

Time-series ML

Software engineering

Education

Bachelor's/Master's in CS/Applied Math/Statistics/Aerospace/Physics or related field

Tools

Git

Testing

Reproducible environments

Job description

The Judge Group is seeking a Data Science Engineer to develop ML components and the integration infrastructure that support space-domain awareness analytics pipelines. On-site in Westminster, CO you will build trajectory-classification and anomaly-detection models that operate on orbit-determination output.

You will also build benchmarking and evaluation frameworks to ensure pipeline performance under sparse, noisy observations while collaborating with astrodynamics and embedded-systems teams to