Data Science Engineer

Responsibilities:

• Develop AI/ML models for trajectory classification across orbit regimes and families, and for detection of anomalous dynamical behavior.

• Integrate and maintain end-to-end analytical pipelines spanning observation processing, hypothesis generation, orbit estimation, propagation, and classification; define interfaces, own the shared, reproducible codebase.

• Build benchmarking and evaluation frameworks: estimator convergence behavior, classification accuracy and confusion structure, false-positive and false-negative characterization, time-to-custody, and sensitivity to track gaps and elevated measurement uncertainty.

• Design experiments that distinguish genuine generalization from dataset artifacts 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 operator decisions.

• Partner with embedded-systems staff to characterize model complexity, memory footprint, and inference latency, and to identify quantization, pruning, or architectural simplifications that perform within deployment constraints.

• Contribute machine-learning inputs to CONOPS and systems-engineering activities, including data-flow definition, model lifecycle and retraining considerations, and critical technology element identification.

Qualifications:

• BS/MS in Computer Science, Applied Mathematics, Statistics, Aerospace Engineer, Physics, or a related quantitative field, plus [4]+ years of applied ML experience (or equivalent).

• Strong Python and the scientific stack (NumPy, SciPy, pandas); fluency in at least one deep-learning framework (PyTorch preferred).

• Demonstrated experience building ML systems on time-series, sequential, or state-estimation-adjacent data, rather than tabular or vision benchmarks alone.

• Sound evaluation methodology: class imbalance, calibration, uncertainty quantification, and the failure modes of small or synthetically generated datasets.

• Software-engineering discipline sufficient for a shared codebase: version control, testing, reproducible environments, and documented interfaces.

• Clear technical writing for customer-facing deliverables.

• Must be able to obtain and hold a U.S. security clearance

Preferred Qualities:

• Experience with physics-informed ML, or hybrid approaches that embed dynamical structure into learned models.

• Familiarity with orbit determination, tracking, or multi-target data association (JPDA, MHT, or similar).

• Experience with model compression, quantization, or deployment to constrained and embedded targets.

• Prior work on government R&D programs (SBIR/STTR, AFRL, DARPA, Space Force) and familiarity with TRL terminology.

• Experience with anomaly detection where anomalies are rare, poorly labeled, or defined only by a physical model.

General Requirements:

• Writing and delivering technical documents and briefings.

• Verbal and written communication skills as well as organizational skills.

• Must be a U.S. citizen.

• Travel occasionally as necessary.