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Don't Wait for the Future. Build it Here.

We seek the curious, the brilliant, and the relentless. If you’re driven to solve complex problems, operate at the edge of technology, and make systems smarter, faster, and safer, you’ll find a home here. Advance your career in a company that builds for what's next.

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We’re a team of high performers who don’t settle. Our culture rewards curiosity, integrity, and results—without the ego. You’ll be surrounded by people who challenge you, support you, and celebrate your wins. We work hard, solve big problems, and have each other’s backs.

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We believe exceptional work deserves exceptional rewards. Our total compensation goes beyond base pay to include robust benefits, performance incentives, and investment in your future. From health and retirement to career development and time off—you’ll have what you need to thrive, in and out of the office.

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From entry-level engineers to mission program leads, we create space for every employee to thrive. Our work is complex, our standards are high, and our support systems are built to help you rise—wherever you're starting from.

People at Aurex

“I enjoy working at Aurex for the innovative environment and strong focus on work-life balance… The company values creativity, supports professional growth, and fosters collaboration. I feel empowered to make a difference, both through meaningful projects and through volunteering in my community.”

People at Aurex

“I love being part of the platform because it offers the resources and opportunities of a larger company while maintaining a tight-knit, small company culture. What motivates me most is the environment it creates, one that supports me in growing professionally, academically, and personally.”

jacob Ballentine
Junior Reverse Engineer

People at Aurex

“What I love about working with Aurex is that I always feel valued and appreciated, and that work never goes unrecognized … Another thing I love is that, though small, Aurex offers tremendous expertise in various fields … Finally, the diversity of projects and partners means … many different opportunities—you will be helping to create history.”

Nathaniel DeCecco

Mechanical Engineer

People at Aurex

“What I like most about the company is the strong sense of community… It truly feels like a place where I belong. I’m motivated by the chance to grow, learn, and take on new challenges… Being part of a passionate, driven team makes every day rewarding.”

People at Aurex

“I appreciate that our company focuses on growth and innovation in aerospace… I’m motivated by solving complex challenges and building reliable software for launch operations. It’s rewarding to know our voice system is used by the government and major contractors—making a real impact. What a dream!”

Astrid Leighton
Software Engineer

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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 matter—fast. 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 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.

Basic Qualifications

• 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 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.
• Meaningful hands‑on experience developing, training, and evaluating reinforcement-learning agents for sequential decision‑making, planning, control, or autonomous‑system applications.
• Strong Python software‑development experience.
• Practical experience with at least one modern deep‑learning framework, such as PyTorch, JAX, or TensorFlow.
• Experience creating or adapting simulation environments for learning agents, including defining observations, actions, objectives or rewards, constraints, scenarios, and evaluation metrics.
• Strong understanding of core reinforcement‑learning concepts, including exploration, credit assignment, policy evaluation, training stability, generalization, and agent‑environment interaction.
• Experience working with continuous, discrete, or hybrid decision problems.
• Experience with decision‑making under uncertainty, stochastic environments, or partial observability.
• Experience integrating learned agents, algorithms, or software services with physics‑based models, simulations, test harnesses, or larger software systems.
• Proficiency with modern software‑development practices, including source control using Git, code reviews, automated or unit testing, software organization, and reproducible experimentation.
• Demonstrated ability to communicate complex AI, software, and engineering concepts to multidisciplinary technical teams.
• Ability to provide technical leadership and contribute effectively in a collaborative engineering environment.
• Active Secret security clearance or higher.
• Ability to work on‑site at an Aurex office in Huntsville, Alabama.

Preferred Qualifications

• Master’s degree or Ph.D. in Computer Science, Aerospace Engineering, Electrical Engineering, Robotics, Applied Mathematics, Operations Research, or a closely related technical discipline.
• 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.
• Experience with multi‑agent reinforcement learning, cooperative or adversarial agents, distributed decision‑making, or game‑theoretic methods.
• 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.
• Experience with distributed or large‑scale RL training, including parallel simulation, distributed rollouts, GPU acceleration, cluster computing, or scalable experiment infrastructure.
• Experience with RL libraries or frameworks such as Ray/RLlib, Stable‑Baselines3, CleanRL, TorchRL, Gymnasium, PettingZoo, or comparable internally developed frameworks.
• Experience integrating reinforcement learning with classical control, trajectory optimization, mathematical programming, search, planning, or model‑predictive control.
• Knowledge of partially observable Markov decision processes, belief‑state estimation, stochastic optimal control, or decision‑making under uncertainty.
• Experience developing high‑fidelity, physics‑based, hardware‑in‑the‑loop, software‑in‑the‑loop, or distributed simulation environments.
• Experience with Monte Carlo analysis, design of experiments, uncertainty quantification, verification and validation, sensitivity analysis, or statistical performance assessment.
• Experience transitioning AI or autonomy algorithms from research or simulation environments into real‑time or operational software systems.
• Familiarity with real‑time software constraints, deterministic execution, latency management, fault handling, runtime assurance, or graceful fallback architectures.
• Experience with containerized and reproducible development environments using technologies such as Docker, Linux, CI/CD pipelines, or cloud/HPC computing environments.
• Experience leading technical efforts, mentoring engineers, defining technical approaches, or serving as a technical lead on multidisciplinary engineering programs.
• Experience supporting Department of Defense, intelligence community, aerospace, or other U.S. Government programs.
• Active Top Secret or TS/SCI security clearance.

How You Will Be Rewarded

The salary range for this role is $170,000.00 - $200,000.00 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 candidate’s work experience, education, training, and relevant skills. We offer a competitive benefits package designed to support our employees' health, well‑being, and professional growth.

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 applicant’s 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 U.S. 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.

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