Astrodynamics Engineer: Cislunar & SDA Modeling

Digantara; Denver (CO); On-site; USD 120,000 - 180,000; Full time

Job summary

Digantara US seeks an experienced Astrodynamics Engineer to act as the technical authority for orbital mechanics across SDA programs. You will build dynamical models, generate synthetic datasets, and review failure modes prior to customer delivery.

The role involves collaboration with research partners on cislunar dynamics, CR3BP analysis, and multi-body propagation, ensuring robust interfaces, validation, and data quality for ML development.

Qualifications

• MS or PhD in Aerospace Engineering, Astrodynamics, Applied Mathematics, or a related field, plus 3+ years of relevant experience.
• Demonstrated orbit-determination experience with batch least squares and Kalman-family estimators, including covariance & observability.
• Working knowledge of astrodynamics across LEO/GEO and multi-body dynamics (CR3BP, libration-point dynamics, invariant manifolds).

Responsibilities

• Build and validate dynamical modeling environments across mission regimes, from two-body to multi-body cislunar dynamics.
• Generate synthetic observation datasets for ML model training and evaluation with realistic sensing geometry and measurement noise.
• Implement and characterize orbit-estimation chains with proper covariance treatment and observability analysis.
• Collaborate with research partners to define interfaces, conventions, and validation cases, ensuring clear ownership boundaries.
• Provide physics-derived inputs to CONOPS and systems engineering activities regarding latency and timing constraints.
• Serve as technical point of contact for university/research-institution collaborators.

Skills

Python

C/C++

MATLAB

Julia

Education

MS or PhD in Aerospace Engineering / Astrodynamics

Tools

Python

C/C++

MATLAB

Julia

Job description

Digantara US seeks an experienced Astrodynamics Engineer to act as the technical authority for orbital mechanics across SDA programs. You will build dynamical models, generate synthetic datasets, and review failure modes prior to customer delivery.

The role involves collaboration with research partners on cislunar dynamics, CR3BP analysis, and multi-body propagation, ensuring robust interfaces, validation, and data quality for ML development.