Autonomy Engineer, Ops Research (Senior - Principal)

YOUR MISSION

As a member of the Applied Algorithms and Autonomy team, you will design, build, and deploy core autonomy capabilities for True Anomaly. You will work with a talented cross-functional team to advance technology at the intersection of artificial intelligence, machine learning, and classical optimization. This will involve hands-on development across various areas including fleet scheduling, vehicle autonomy, mission planning, wargaming, threat assessment, and uncooperative RPO capabilities. You are a first principles engineer who takes ownership of the systems you build and delivers results.  

• →Design, implement, and validate optimization algorithms for fleet-level mission planning, resource allocation, and sequential decision-making under uncertainty 
• →Contribute to system architecture for large-scale distributed optimization problems, informed by statistical modeling, simulation-based analysis, and operational constraints 
• →Collaborate with cross-functional teams to formalize stakeholder requirements into mathematical programs and deploy scalable solutions 
• →Tune and validate optimization models through simulation, hardware-in-the-loop testing, and operational deployment 
• →Develop production-quality implementations with rigorous documentation and testing  

• Bachelor's degree in operations research, applied mathematics, computer science, aerospace engineering, electrical engineering, or related quantitative discipline 
• Proficient in C/C++ and Python for implementing optimization solvers and numerical methods 
• Strong expertise in at least one domain: 
•   Adversarial optimization: game theory, Nash equilibria, minimax optimization, sequential games, adversarial search 
•   Mathematical programming: model predictive control, trajectory optimization, dynamic programming, stochastic control, mixed-integer programming, convex optimization 
•   Statistical learning: reinforcement learning, online learning, classification/regression under uncertainty, anomaly detection, predictive modeling 
•   Distributed optimization: fleet coordination, consensus protocols, multi-agent resource allocation, network flow optimization, decentralized control 
• Solid foundation in probability theory, optimization, and stochastic decision processes 
• 4+ years implementing and deploying optimization algorithms in operational systems with real-world constraints 
• Demonstrated ability to formulate complex problems as tractable mathematical programs and collaborate across disciplines 
• Passion for space operations and advancing capabilities in space domain awareness  

• Master's or PhD in operations research, applied mathematics, computer science, aerospace engineering, or related discipline 
• Experience with high-performance numerical computing and production-grade solver implementations 
• Familiarity with edge computing constraints and real-time optimization under latency bounds 
• Background in astrodynamics, orbital mechanics, or spacecraft operations 
• Experience with Bayesian inference, state estimation (Kalman filtering, particle methods), and planning under partial observability 
• Track record in verification/validation of mission-critical optimization systems 
• Understanding of how game-theoretic, optimization, and learning-based approaches compose for robust decision-making 

• Work Location—this is a fully onsite role. Candidates must be based in or able to commute to our Denver or Long Beach office daily. 
• Work environment—the work environment; temperature, noise level, inside or outside, or other factors that will affect the person's working conditions while performing the job.
• Physical demands—the physical demands of the job, including bending, sitting, lifting and driving.

This position will be open until it is successfully filled. To submit your application, please follow the directions below. #LI-Onsite