Sr. Reinforcement Learning & Autonomous Decision Systems Engr

Senior Reinforcement Learning & Autonomous Decision Systems Engineer
Huntsville, AL

Aurex is a mission-focused aerospace and defense company building the next frontier of deterrence. From hypersonics and missile defense to hardened networks and orbital systems, we design, test, and deliver the platforms that turn unproven ideas into battlefield-ready capability.

Born in Huntsville and built for speed, Aurex brings together aerospace veterans, combat-tested operators, and forward-leaning technologists to solve problems that matter—fast. We move from whiteboard to warfighter with precision, clarity, and zero tolerance for fluff.

Aurex is seeking a Senior Reinforcement Learning / AI Engineer to develop reinforcement-learning and AI-enabled decision systems for complex aerospace and defense applications. This role is centered on intelligent agents that make closed-loop decisions over time in simulation and, ultimately, in mission-relevant real-time environments.

The work may include continuous control, discrete and hybrid decision spaces, planning, coordination, and decision-making under uncertainty and partial observability.

The successful candidate will formulate decision problems, design learning environments, train and evaluate agents, and integrate learned policies with physics-based models and operational simulations. This is not primarily a perception or computer-vision role; the emphasis is on sequential decision-making, autonomous behavior, and rigorous engineering evaluation.

• Design, implement, train, and evaluate reinforcement-learning agents for mission planning, guidance and control, resource allocation, engagement management, battle management, and other autonomous decision problems.

• Translate operational and engineering problems into rigorous sequential-decision formulations, including states and observations; continuous, discrete, or hybrid action spaces; objectives and rewards; constraints; termination conditions; and uncertainty models.

• Build and maintain simulation-based learning environments that connect agents to vehicle, sensor, weapon, threat, environmental, command-and-control, guidance, navigation, and control models.

• Develop end-to-end training and evaluation workflows, including scenario generation, parallel rollouts, experiment tracking, checkpointing, regression baselines, reproducibility, and analysis of agent behavior.

• Train, tune, and debug agents, identifying issues such as training instability, poor exploration, reward misspecification, overfitting, weak generalization, and unintended exploitation of simulation behavior.

• Assess tradeoffs among model-free reinforcement learning, model-based learning, planning, classical control, optimization, and hybrid approaches, selecting methods based on mission and engineering requirements.

• Design evaluation campaigns to assess performance, robustness, generalization, uncertainty, edge cases, failure modes, interpretability, traceability, and operational relevance.

• Address real-time execution requirements, including inference latency, action constraints, deterministic interfaces, runtime monitoring, graceful fallback behavior, and integration with mission software.

• Use Monte Carlo analysis, sensitivity studies, trade studies, and controlled experiments to characterize agent performance and simulation assumptions.

• Collaborate with modeling and simulation engineers, software developers, systems engineers, analysts, and subject‑matter experts to translate operational questions into executable learning and evaluation experiments.

• Apply modern software‑engineering practices and AI‑assisted development tools to accelerate prototyping, testing, refactoring, and documentation while maintaining engineering rigor.

• Provide technical leadership, mentor other engineers, and document architectures, methods, assumptions, interfaces, experiments, results, and recommendations.

• Bachelor's degree in Computer Science, Computer Engineering, Aerospace Engineering, Electrical Engineering, Mechanical Engineering, Physics, Applied Mathematics, or a related technical field.

• Ten or more years of…