Location: Westminster, CO
Salary: $120,000.00 USD Annually - $180,000.00 USD Annually
Description: Astrodynamics EngineerLocation: Westminster, CO 80031
Employment Type: Full-Time, Permanent
Job SummaryWe are seeking an experienced
Astrodynamics Engineer to serve as a technical authority for orbital mechanics across space domain awareness programs. This position will develop and validate physical models, synthetic datasets, orbit-estimation methods, and state-estimation capabilities supporting missions across low Earth orbit, geosynchronous Earth orbit, cislunar space, and other complex orbital regimes.
The Astrodynamics Engineer will generate synthetic datasets used to develop, train, validate, and evaluate machine-learning models. This individual will also review datasets and model results to identify modeling limitations, unrealistic assumptions, and potential failure modes before solutions are delivered for operational use.
This role will serve as a primary technical interface with university and research-institution partners supporting cislunar dynamical modeling, orbit determination, and multi-body dynamics research. Responsibilities include establishing technical interfaces, defining modeling conventions, developing validation cases, documenting requirements, and clarifying ownership across collaborative research efforts.
Key Responsibilities- Build and validate astrodynamics modeling environments covering multiple mission regimes, including:
- Two-body orbital dynamics
- Perturbed low Earth orbit and geosynchronous Earth orbit propagation
- Cislunar and multi-body dynamics
- Circular restricted three-body problem models
- Higher-fidelity orbital dynamics models
- Generate representative synthetic observation datasets across orbital regimes and orbit families.
- Model realistic sensing conditions, including:
- Observation geometry
- Sensor visibility
- Lighting constraints
- Measurement noise
- Track gaps
- Sparse or degraded observations
- Develop synthetic datasets for algorithm development and machine-learning model training, testing, and evaluation.
- Incorporate publicly available ephemerides and reference datasets as independent validation cases.
- Develop, implement, and evaluate orbit-determination and state-estimation methods appropriate to each mission scenario.
- Apply batch least-squares, sequential estimation, Kalman filtering, and related estimation techniques.
- Evaluate covariance, uncertainty, observability, and estimator convergence.
- Determine the conditions under which orbital custody can be established or maintained and identify scenarios in which custody may not be achievable.
- Analyze sparse-observation and too-short-arc scenarios using initial orbit determination, admissible regions, and hypothesis-generation techniques.
- Define anomalous orbital or dynamical behavior using measurable physical characteristics and documented criteria.
- Partner with data science and software engineering teams to ensure training and evaluation datasets are physically accurate and dynamically representative.
- Review machine-learning model results against orbital mechanics and physical expectations.
- Provide physics-based inputs to concepts of operations and systems-engineering activities.
- Evaluate revisit intervals, latency requirements, timing constraints, sensing limitations, and estimation performance.
- Document modeling assumptions, validation methods, limitations, uncertainty, and technical conclusions.
- Serve as a primary technical point of contact for university and research-institution partners.
- Establish technical interfaces, modeling conventions, validation cases, deliverables, and ownership responsibilities for external research collaborations.
Required Qualifications- Master's degree or PhD in Aerospace Engineering, Astrodynamics, Applied Mathematics, or a related technical field.
- At least 3 years of relevant professional, research, or academic experience. A PhD may substitute for a portion of the experience requirement.
- Demonstrated experience with orbit determination and state-estimation methods, including:
- Batch least-squares estimation
- Sequential estimation
- Kalman-family estimators
- Covariance analysis
- Observability analysis
- Working knowledge of astrodynamics across multiple orbital regimes, including:
- Perturbed near-Earth dynamics
- Low Earth orbit
- Geosynchronous Earth orbit
- Multi-body orbital dynamics
- Circular restricted three-body problem dynamics
- Libration-point dynamics
- Invariant manifolds
- Periodic orbit families
- Resonance structures
- Experience addressing sparse-observation and too-short-arc orbit-determination problems.
- Experience with admissible regions, initial orbit determination, and hypothesis generation and pruning.
- Strong Python programming skills or the demonstrated ability to quickly develop advanced Python capabilities.
- Familiarity with C++, C, MATLAB, or Julia for propagator, simulator, estimator, or scientific software development.
- Knowledge of optical observation modeling, including measurement types, sensor error sources, visibility constraints, and lighting conditions.
- Ability to document assumptions, uncertainty, validation results, and technical conclusions clearly.
- Ability to collaborate effectively with software engineers, data scientists, systems engineers, and external research partners.
- Ability to obtain and maintain a U.S. Government security clearance.
Preferred Qualifications- Direct experience with cislunar or extended geosynchronous orbit space domain awareness applications.
- Experience with conventional low Earth orbit or geosynchronous Earth orbit custody, tracking, and catalog-maintenance programs.
- Familiarity with normal-form methods, Lie series, or other model-reduction techniques applied to multi-body dynamics.
- Previous experience collaborating with universities or research institutions on government-funded programs.
- Experience generating synthetic datasets for machine-learning applications.
- Understanding of potential data leakage, artificial patterns, and unintended structure within synthetic training datasets.
- Experience evaluating machine-learning outputs against physics-based models or expected orbital behavior.
- Publications or conference presentations in astrodynamics, orbital mechanics, or space domain awareness.
- Experience presenting technical work at professional venues such as AAS, AIAA, or AMOS.
Work Environment- Full-time, permanent position based in Westminster, Colorado.
- Work involves collaboration with multidisciplinary engineering, software, data science, systems engineering, and research teams.
- The position may support government, aerospace, defense, or national-security-related programs.
- Employment is subject to applicable eligibility and security-clearance requirements.
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