AI​/ML​/RL Scientist

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?

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!

We are the Intelligent Combat Systems Group, and our mission focus is to ensure our Nation maintains the operational advantage on the future battlefield through foundational advances in artificial intelligence, autonomy, manned‑unmanned teaming and novel unmanned aircraft (e.g. drones) design and testing. We believe the future of warfare will be defined by intelligent autonomous systems capable of fighting with machine precision at machine speeds.

Whether it is developing the intelligence that drives autonomous wingmen behaviors, integrated real‑time collaboration tools and data analytic architectures, or novel AI design tools and software, the Intelligent Combat Systems Group is at the forefront. Three of our recent game‑changing projects (DARPA Air Combat Evolution, AFRL Golden Horde, and Air Force Sky Borg) are featured in recent news articles, highlighting our impact and innovation.

We are seeking inquisitive and creative team members who like to tackle challenging problems to help us build the next generation of autonomous combat systems and shape the future of warfare. Our team is an entrepreneurial and multidisciplinary team committed to developing technical talent, fostering a culture of innovation and collaboration, while having fun with what we do!

You will:

• 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.

You meet the minimum requirements for the job if you...

• 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,…