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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Reinforcement Learning Engineer - **Company:** The Johns Hopkins University Applied Physics Laboratory LLC - **Location:** United States - **Experience:** Experienced - **Salary:** $100,000.0 - $245,000.0 - **Contract:** Contract - **Skills:** Artificial Intelligence, Data Analysis, Dynamical Systems, Game Theory, Python (Programming Language), MATLAB, Machine Learning, Tensorflow, Software Engineering, Reinforcement Learning, Pytorch, Multi-Agent Systems, Virtual Reality, Deep Learning, Information Technology - **Published:** August 14, 2026 - **Apply:** https://www.dice.com/job-detail/ba7c600a-ed90-49ea-8f68-f98e14714dca ## About the Role * Hold a Bachelor's degree in Aerospace Engineering, Electrical Engineering, Mechanical Engineering, Computer Science, Mathematics, Physics or a related technical field. * Have at least 2+ years of professional, hands-on experience applying machine learning techniques to challenging problems. * Possess direct experience or significant academic project work in Reinforcement Learning. * Are proficient in Python and have hands-on experience with at least one major deep learning framework (e.g., PyTorch, TensorFlow). * Have a solid understanding of the mathematical foundations of ML, including probability, statistics, and linear algebra. * Are able to obtain an Interim Secret level security clearance by your start date and can ultimately obtain a TS/SCI level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship. You'll go above and beyond our minimum requirements if you... * Hold a Master's degree or PhD in Aerospace Engineering, Electrical Engineering, Mechanical Engineering, Computer Science, Mathematics, Physics or a related technical field. * Have experience with advanced RL topics such as multi-agent RL (MARL), inverse RL (IRL), or hierarchical RL (HRL). * Possess a background in control theory (e.g., Model Predictive Control, optimal control), game theory, or dynamical systems * Have demonstrated experience with robotics or aerospace simulation platforms (e.g., Gazebo, AirSim, AFSIM, MATLAB/Simulink). * Have demonstrated experience applying advanced data analysis techniques or explainable AI to understand complex system behaviors. * Have contributed to publications or presentations at relevant AI or robotics conferences. * Hold an active TS/SCI level security clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship. ## Description Are you interested in working in multi-disciplinary teams to advance the state-of-the-art in autonomous systems, uncrewed air systems, artificial intelligence, software design, embedded systems, virtual reality, and simulation?, * Design, implement, and train reinforcement learning (RL) agents for complex, multi-agent collaborative and competitive tasks in the aerospace and defense domain. * Develop novel solutions for uncrewed aerial systems (UAS) and drones, enabling sophisticated autonomous behaviors like coordinated flight, resource allocation, and adaptive tactics. * Integrate and test intelligent agents within high-fidelity simulation environments, analyzing emergent behaviors, performance metrics, and system robustness under various conditions. * Apply your knowledge of reinforcement learning, game theory, dynamical systems, and/or control theory to build agents that are not only intelligent but also stable and physically plausible. * Collaborate with a cross-functional team of AI researchers, robotics engineers, and domain experts to translate mission objectives into solvable RL problems. * Contribute to the full research and development lifecycle, from algorithm selection and experimentation to the analysis and presentation of results. ## Related Videos - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Leveraging Large Language Models for Legacy Code Translation: Challenges and Solutions](https://www.wearedevelopers.com/videos/1157-leveraging-large-language-models-for-legacy-code-translation-challenges-and-solutions) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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