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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Reinforcement Learning / AI Engineer - **Company:** Aurex Platform - **Location:** Huntsville, AL, United States - **Experience:** Expert - **Salary:** $170,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Software Applications, Computing Platforms, Unit Testing, Cloud Computing, Computer Clusters, Code Review, Computer Engineering, Continuous Integration, Learning Management Systems, Linux, Hardware-In-The-Loop Simulation, Python (Programming Language), Machine Learning, Mathematical Programming, Monte Carlo Methods, Real-Time Operating Systems, Distributed Simulation, Tensorflow, Software Systems, Verification and Validation (Software), Reinforcement Learning, Software Organization, Pytorch, Multi-Agent Systems, Deep Learning, Git, Information Technology, Modeling and Simulation, Markov, Software Version Control, Docker - **Published:** August 17, 2026 - **Apply:** https://jobs.military.com/career/309124/sr-reinforcement-learning-autonomous-decision-systems-engineer-alabama-al-huntsville ## About the Role * Bachelor's degree in Computer Science, Computer Engineering, Aerospace Engineering, Electrical Engineering, Mechanical Engineering, Physics, Applied Mathematics, or a related technical field. \n * Ten or more years of relevant professional experience in reinforcement learning, autonomy, machine learning, robotics, control systems, modeling and simulation, or related engineering disciplines. Additional relevant education may substitute for experience. \n * Meaningful hands-on experience developing, training, and evaluating reinforcement-learning agents for sequential decision-making, planning, control, or autonomous-system applications. \n * Strong Python software-development experience. \n * Practical experience with at least one modern deep-learning framework, such as PyTorch, JAX, or TensorFlow. \n * Experience creating or adapting simulation environments for learning agents, including defining observations, actions, objectives or rewards, constraints, scenarios, and evaluation metrics. \n * Strong understanding of core reinforcement-learning concepts, including exploration, credit assignment, policy evaluation, training stability, generalization, and agent-environment interaction. \n * Experience working with continuous, discrete, or hybrid decision problems. \n * Experience with decision-making under uncertainty, stochastic environments, or partial observability. \n * Experience integrating learned agents, algorithms, or software services with physics-based models, simulations, test harnesses, or larger software systems. \n * Proficiency with modern software-development practices, including source control using Git, code reviews, automated or unit testing, software organization, and reproducible experimentation. \n * Demonstrated ability to communicate complex AI, software, and engineering concepts to multidisciplinary technical teams. \n * Ability to provide technical leadership and contribute effectively in a collaborative engineering environment. \n * Active Secret security clearance or higher. \n * Ability to work on-site at an Aurex office in Huntsville, Alabama.\n, * Master's degree or Ph.D. in Computer Science, Aerospace Engineering, Electrical Engineering, Robotics, Applied Mathematics, Operations Research, or a closely related technical discipline. \n * Advanced experience with modern reinforcement-learning methods, including actor-critic approaches, policy-gradient methods, value-based methods, offline RL, model-based RL, or hierarchical reinforcement learning. \n * Experience with multi-agent reinforcement learning, cooperative or adversarial agents, distributed decision-making, or game-theoretic methods. \n * Experience designing reinforcement-learning systems for aerospace, defense, autonomous vehicles, robotics, guidance and control, mission planning, battle management, or other safety- or mission-critical applications. \n * Experience with distributed or large-scale RL training, including parallel simulation, distributed rollouts, GPU acceleration, cluster computing, or scalable experiment infrastructure. \n * Experience with RL libraries or frameworks such as Ray/RLlib, Stable-Baselines3, CleanRL, TorchRL, Gymnasium, PettingZoo, or comparable internally developed frameworks. \n * Experience integrating reinforcement learning with classical control, trajectory optimization, mathematical programming, search, planning, or model-predictive control. \n * Knowledge of partially observable Markov decision processes, belief-state estimation, stochastic optimal control, or decision-making under uncertainty. \n * Experience developing high-fidelity, physics-based, hardware-in-the-loop, software-in-the-loop, or distributed simulation environments. \n * Experience with Monte Carlo analysis, design of experiments, uncertainty quantification, verification and validation, sensitivity analysis, or statistical performance assessment. \n * Experience transitioning AI or autonomy algorithms from research or simulation environments into real-time or operational software systems. \n * Familiarity with real-time software constraints, deterministic execution, latency management, fault handling, runtime assurance, or graceful fallback architectures. \n * Experience with containerized and reproducible development environments using technologies such as Docker, Linux, CI/CD pipelines, or cloud/HPC computing environments. \n * Experience leading technical efforts, mentoring engineers, defining technical approaches, or serving as a technical lead on multidisciplinary engineering programs. \n * Experience supporting Department of Defense, intelligence community, aerospace, or other U.S. Government programs. \n * Active Top Secret or TS/SCI security clearance.\n ## Related Videos - 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