Autonomy Engineer, Ops Research (Senior - Principal)

<p>Space is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it.</p>

<p><u>OUR MISSION</u></p>

<p>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.</p>

<p><u>OUR VALUES</u></p>

<ul>

<li><strong>Be the offset.</strong>&nbsp;We create asymmetric advantages with creativity and ingenuity.</li>

<li><strong>What would it take?</strong>&nbsp;We challenge assumptions to deliver ambitious results.</li>

<li><strong>It’s the people.</strong>&nbsp;Our team is our competitive advantage and we are better together.</li>

</ul><p><u>YOUR MISSION</u></p>

<p>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. &nbsp;</p>

<p><strong>RESPONSIBILITIES</strong></p>

<ul>

<li>Design, implement, and&nbsp;validate&nbsp;optimization algorithms for fleet-level mission planning, resource allocation, and sequential decision-making under uncertainty&nbsp;</li>

<li>Contribute to system architecture for large-scale distributed optimization problems, informed by statistical modeling, simulation-based analysis, and operational constraints&nbsp;</li>

<li>Collaborate with cross-functional teams to formalize stakeholder requirements into mathematical programs and deploy scalable solutions&nbsp;</li>

<li>Tune and&nbsp;validate&nbsp;optimization models through simulation, hardware-in-the-loop testing, and operational deployment&nbsp;</li>

<li>Develop production-quality implementations with rigorous documentation and testing &nbsp;</li>

</ul>

<p><strong>QUALIFICATIONS</strong></p>

<ul>

<li>Bachelor's degree in operations research, applied mathematics, computer science, aerospace engineering, electrical engineering, or related quantitative discipline&nbsp;</li>

<li>Proficient in C/C++ and Python for implementing optimization solvers and numerical methods&nbsp;</li>

<li>Strong&nbsp;expertise&nbsp;in at least one domain:&nbsp;</li>

<li>&nbsp; Adversarial optimization: game theory, Nash equilibria, minimax optimization, sequential games, adversarial search&nbsp;</li>

<li>&nbsp; Mathematical programming: model predictive control, trajectory optimization, dynamic programming, stochastic control, mixed-integer programming, convex optimization&nbsp;</li>

<li>&nbsp; Statistical learning: reinforcement learning, online learning, classification/regression under uncertainty, anomaly detection, predictive modeling&nbsp;</li>

<li>&nbsp; Distributed optimization: fleet coordination, consensus protocols, multi-agent resource allocation, network flow optimization, decentralized control&nbsp;</li>

<li>Solid foundation in probability theory, optimization, and stochastic decision processes&nbsp;</li>

<li>4+ years implementing and deploying optimization algorithms in operational systems with real-world constraints&nbsp;</li>

<li>Demonstrated ability to formulate complex problems as tractable mathematical programs and collaborate across disciplines&nbsp;</li>

<li>Passion for space operations and advancing capabilities in space domain awareness &nbsp;</li>

</ul>

<p><strong>PREFERRED SKILLS AND EXPERIENCE</strong></p>

<ul>

<li>Master's or PhD in operations research, applied mathematics, computer science, aerospace engineering, or related discipline&nbsp;</li>

<li>Experience with high-performance numerical computing and production-grade solver implementations&nbsp;</li>

<li>Familiarity with edge computing constraints and real-time optimization under latency bounds&nbsp;</li>

<li>Background in astrodynamics, orbital mechanics, or spacecraft operations&nbsp;</li>

<li>Experience with Bayesian inference, state estimation (Kalman filtering, particle methods), and planning under partial observability&nbsp;</li>

<li>Track record&nbsp;in verification/validation of mission-critical optimization systems&nbsp;</li>

<li>Understanding of how game-theoretic, optimization, and learning-based approaches compose for robust decision-making&nbsp;</li>

</ul>

<p><strong>COMPENSATION</strong></p>

<ul>

<li><strong>Base Salary:</strong> $180,000 - $360,000&nbsp;</li>

<li><strong>Equity + Benefits</strong> including Health, Dental, Vision, HRA/HSA options, PTO and paid holidays, 401K, Parental Leave&nbsp;</li>

</ul>

<p><em>Your actual level and base salary will be&nbsp;determined on a case-by-cas</em></p>