Astrodynamics Engineer (1144732)

Client U.S. is a leading Space Surveillance and Intelligence company focused on ensuring orbital safety and sustainability. With expertise in space-based detection, tracking, identification, and monitoring, Client provides comprehensive domain awareness across regimes, allowing end users to have actionable intelligence on a single platform. At the core of its infrastructure lies a sophisticated integration of hardware and software capabilities aligned with the key principles of situational awareness: perception (data collection), comprehension (data processing), and prediction (analytics). This holistic approach empowers Client to monitor all Resident Space Objects (RSOs) in orbit, fostering comprehensive domain awareness.

About the role

Client U.S. is seeking an experienced and driven Astrodynamics Engineer to serve as the technical authority for orbital mechanics across the company's Space Domain Awareness (SDA) programs. The physical models, synthetic datasets, and estimated states that underpin these efforts trace back to this role — spanning cislunar and multi-body dynamics as well as more conventional orbit determination in the LEO and GEO regimes. You will generate the synthetic datasets used to train and evaluate machine-learning models, serving as both the source of that data and its most rigorous reviewer. You will act as a primary technical interface to the company's research institution partners, whose contributions span areas such as cislunar dynamical modeling, MOSS-based orbit determination, and CR3BP normal-form analysis.

Responsibilities

• Build and validate dynamical modeling environments spanning the company's mission regimes — from conventional two-body and perturbed LEO/GEO propagation through multi-body cislunar dynamics (CR3BP and higher-fidelity models).
• Generate representative synthetic observation datasets across orbit regimes and families, including realistic sensing geometry, visibility constraints, measurement noise, track gaps, and degraded-observation scenarios, for use in algorithm development and machine-learning model training and evaluation.
• Incorporate publicly available ephemerides and reference datasets as independent validation cases against synthetic data generators.
• Implement and characterize orbit-estimation chains appropriate to each problem with honest treatment of covariance and observability.
• Characterize estimator convergence behavior, observability limits, and the conditions under which custody is achievable versus infeasible; document results.
• Define anomalous dynamical behavior in physical terms, so that downstream detection and classification methods operate against a precise definition rather than an intuition.
• Partner with data science and software staff to ensure training and evaluation datasets are dynamically meaningful, and to review model results against physical expectation.
• Provide physics-derived inputs to CONOPS and systems-engineering activities — revisit intervals, dynamically driven latency and timing constraints, and realistic bounds on what a given sensing and estimation architecture can conclude.
• Serve as a primary technical point of contact for research institution partners supporting the company's astrodynamics work.

Requirements

• MS or PhD in Aerospace Engineering, Astrodynamics, Applied Mathematics, or a related field, plus 3+ years of relevant experience (PhD may substitute for a portion).
• Demonstrated orbit determination experience across common estimation approaches — batch least squares and sequential/Kalman-family estimators — including rigorous treatment of covariance and observability.
• Working knowledge of astrodynamics across regimes, including perturbed near-Earth dynamics (LEO/GEO) and multi-body dynamics (CR3BP, libration-point dynamics, invariant manifolds, periodic orbit families, and resonance structure).
• Experience with sparse-observation and too-short-arc problems: admissible regions, initial orbit determination, and hypothesis generation and pruning.
• Strong Python (or the ability to become strong quickly); comfort with C/C++, MATLAB, or Julia as needed for propagator and estimator development.
• Familiarity with optical observation modeling: measurement types, error sources, and visibility and lighting constraints.
• Must be able to obtain and hold a U.S. security clearance.

Preferred Qualifications

• Direct experience with cislunar or XGEO SDA problems, in addition to conventional LEO/GEO custody and catalog-maintenance work.
• Familiarity with normal-form methods, Lie series, or other reduction techniques applied to multi-body dynamics.
• Prior collaboration with university or research-institution partners on government-funded programs.
• Experience generating synthetic datasets intended for machine-learning consumption, with awareness of the ways such datasets can leak structure.
• Publication record in astrodynamics or SDA venues (AAS/AIAA, AMOS).

Pay

$120,000.00 USD Annually - $180,000.00 USD Annually

Location

Westminster, CO