Autonomy Engineer, Ops Research

Responsibilities

• Design, implement, and validate optimization algorithms for fleet-level mission planning, resource allocation, and sequential decision-making under uncertainty.
• Contribute to architectures for large-scale distributed optimization using statistical modeling, simulation-based analysis, and operational constraints.
• Translate stakeholder requirements into mathematical programs and deploy scalable solutions with cross-functional teams.
• 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 a related quantitative discipline.
• Proficiency in C/C++ and Python for implementing optimization solvers and numerical methods.
• Strong expertise in at least one area including adversarial optimization, mathematical programming, statistical learning, or distributed optimization.
• Strong foundation in probability theory, optimization, and stochastic decision processes.
• At least 4 years of experience implementing and deploying optimization algorithms in operational systems with real-world constraints.
• Ability to formulate complex problems as tractable mathematical programs and collaborate across disciplines.
• Preferred qualifications include a master's or PhD, high-performance numerical computing, production-grade solver development, edge computing, real-time optimization, astrodynamics, orbital mechanics, spacecraft operations, Bayesian inference, state estimation, partial-observability planning, and mission-critical system verification and validation.
• Must satisfy applicable U.S. Government space technology export authorization requirements.

Benefits

• Health, dental, vision, and HRA/HSA options.
• PTO and paid holidays.
• 401(k) and parental leave.
• Fully onsite work required, with daily commuting to the Denver or Long Beach office.