AI​/ML​/RL Scientist

AI/ML/RL Scientist – Johns Hopkins Applied Physics Laboratory

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? Are you interested in applying your skills to conceive, design, prototype and test new capabilities in intelligent autonomous systems that will save US warfighter’s lives and ensure our nation’s preeminence? If you answered “yes” to either of these questions, we are looking for someone like you to join our team in the Intelligent Combat Systems Group at APL!

Johns Hopkins Applied Physics Laboratory provided pay range: $/yr – $/yr. This range is provided by Johns Hopkins Applied Physics Laboratory. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

• 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.

• 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, Tensor Flow).
• 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.

• 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, Air Sim, 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.

The Johns Hopkins University Applied Physics Laboratory (APL) brings world‑class expertise to our nation’s most critical defense, security, space and science challenges. While we are dedicated to solving complex…