Sr. Reinforcement Learning & Autonomous Decision Systems Engineer

Job Description

Senior Reinforcement Learning \& Autonomous Decision Systems Engineer Huntsville AL Who We Are 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 matterfast We move from whiteboard to warfighter with precision clarity and zero tolerance for fluff Position Summary 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 Key Responsibilities Design implement train and evaluate reinforcement learning agents for mission planning guidance and control resource allocation engagement management battle management and other autonomous decision problemsTranslate 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 modelsBuild and maintain simulation based learning environments that connect agents to vehicle sensor weapon threat environmental command and control guidance navigation and control modelsDevelop end to end training and evaluation workflows including scenario generation parallel rollouts experiment tracking checkpointing regression baselines reproducibility and analysis of agent behaviorTrain tune and debug agents identifying issues such as training instability poor exploration reward misspecification overfitting weak generalization and unintended exploitation of simulation behaviorAssess tradeoffs among model free reinforcement learning model based learning planning classical control optimization and hybrid approaches selecting methods based on mission and engineering requirementsDesign evaluation campaigns to assess performance robustness generalization uncertainty edge cases failure modes interpretability traceability and operational relevanceAddress real time execution requirements including inference latency action constraints deterministic interfaces runtime monitoring graceful fallback behavior and integration with mission softwareUse Monte Carlo analysis sensitivity studies trade studies and controlled experiments to characterize agent performance and simulation assumptionsCollaborate with modeling and simulation engineers software developers systems engineers analysts and subject matter experts to translate operational questions into executable learning and evaluation experimentsApply modern software engineering practices and AI assisted development tools to accelerate prototyping testing refactoring and documentation while maintaining engineering rigorProvide technical leadership mentor other engineers and document architectures methods assumptions interfaces experiments results and recommendationsBasic Qualifications Bachelors degree in Computer Science Computer Engineering Aerospace Engineering Electrical Engineering Mechanical Engineering Physics Applied Mathematics or a related technical fieldTen 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 experienceMeaningful hands on experience developing training and evaluating reinforcement learning agents for sequential decision making planning control or autonomous system applicationsStrong Python software development experiencePractical experience with at least one modern deep learning framework such as PyTorch JAX or TensorFlowExperience creating or adapting simulation environments for learning agents including defining observations actions objectives or rewards constraints scenarios and evaluation metricsStrong understanding of core reinforcement learning concepts including exploration credit assignment policy evaluation training stability generalization and agent environment interactionExperience working with continuous discrete or hybrid decision problemsExperience with decision making under uncertainty stochastic environments or partial observabilityExperience integrating learned agents algorithms or software services with physics based models simulations test harnesses or larger software systemsProficiency with modern software development practices including source control using Git code reviews automated or unit testing software organization and reproducible experimentationDemonstrated ability to communicate complex AI software and engineering concepts to multidisciplinary technical teamsAbility to provide technical leadership and contribute effectively in a collaborative engineering environmentActive Secret security clearance or higherAbility to work on site at an Aurex office in Huntsville AlabamaPreferred Qualifications Masters degree or PhD in Computer Science Aerospace Engineering Electrical Engineering Robotics Applied Mathematics Operations Research or a closely related technical disciplineAdvanced experience with modern reinforcement learning methods including actor critic approaches policy gradient methods value based methods offline RL model based RL or hierarchical reinforcement learningExperience with multi agent reinforcement learning cooperative or adversarial agents distributed decision making or game theoretic methodsExperience designing reinforcement learning systems for aerospace defense autonomous vehicles robotics guidance and control mission planning battle management or other safety or mission critical applicationsExperience with distributed or large scale RL training including parallel simulation distributed rollouts GPU acceleration cluster computing or scalable experiment infrastructureExperience with RL libraries or frameworks such as RayRLlib Stable Baselines3 CleanRL TorchRL Gymnasium PettingZoo or comparable internally developed frameworksExperience integrating reinforcement learning with classical control trajectory optimization mathematical programming search planning or model predictive controlKnowledge of partially observable Markov decision processes belief state estimation stochastic optimal control or decision making under uncertaintyExperience developing high fidelity physics based hardware in the loop software in the loop or distributed simulation environmentsExperience with Monte Carlo analysis design of experiments uncertainty quantification verification and validation sensitivity analysis or statistical performance assessmentExperience transitioning AI or autonomy algorithms from research or simulation environments into real time or operational software systemsFamiliarity with real time software constraints deterministic execution latency management fault handling runtime assurance or graceful fallback architecturesExperience with containerized and reproducible development environments using technologies such as Docker Linux CICD pipelines or cloudHPC computing environmentsExperience leading technical efforts mentoring engineers defining technical approaches or serving as a technical lead on multidisciplinary engineering programsExperience supporting Department of Defense intelligence community aerospace or other US Government programsActive Top Secret or TSSCI security clearanceHow You Will Be Rewarded The salary range for this role is 17000000 20000000 per year We offer a comprehensive total rewards approach to compensation providing incentives and benefits that extend far beyond the base salary Compensation is determined by the candidates work experience education training and relevant skills We offer a competitive benefits package designed to support our employees health well being and professional growth Location Huntsville AL Aurex is an Equal Opportunity Employer It prohibits discrimination retaliation or any type of harassment on the basis of race color religion gender gender identity or expression sexual orientation national origin genetics disability age veteran status citizenship immigration status or any other legally protected status in employment including in hiring firing and recruiting decisions All applicants must be authorized to work lawfully in the United States for positions at Aurex There may be limited circumstances in which a law regulation executive order or government contract would require certain citizenship; only in those limited circumstances would Aurex require certain citizenship status to comply with the relevant law regulation executive order or government contract applicable to that position For all other positions Aurex does not consider an applicants citizenship but only requires that the applicant be authorized to work lawfully in the United States If a position is one that falls under export control laws and regulations requiring authorization from the US government to access export controlled items any hiring is contingent on the applicant passing the export compliance assessment which is separate from the I 9 process for that specific position A background check will be required prior to any hire Elevate your career by joining the Aurex Platform a leader in aerospace innovation