Autonomy Engineer, Ops Research (Senior - Principal)

About the Company

True Anomaly delivers decisive capabilities for space superiority. We build autonomous spacecraft, advanced payloads, mission software, and space-based interceptors — enabling the U.S. and its Allies to secure the space environment and counter threats from the ultimate high ground.

Our values:

• Be the offset. We create asymmetric advantages with creativity and ingenuity.
• What would it take? We challenge assumptions to deliver ambitious results.
• It’s the people. Our team is our competitive advantage and we are better together.

About the Role

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 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.

Responsibilities

• 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

Requirements

• 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
• U.S. citizenship, lawful permanent residency, or eligibility for required U.S. Government space technology export authorizations (ITAR)

Preferred Qualifications

• 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

Additional Requirements

• This is a fully onsite role. Candidates must be based in or able to commute daily to our Denver or Long Beach office.
• The work environment may involve factors such as temperature, noise level, or other conditions affecting working conditions.
• Physical demands may include bending, sitting, lifting, and driving.

Pay

Base Salary: $180,000 - $360,000

Benefits

• Equity
• Health, Dental, and Vision insurance
• HRA/HSA options
• Paid time off (PTO) and paid holidays
• 401K retirement plan
• Parental Leave