AI/ML/RL Scientist

About the role

We are the Intelligent Combat Systems Group at APL, focusing on foundational advances in artificial intelligence, autonomy, manned-unmanned teaming, and novel unmanned aircraft design and testing. Recent projects like DARPA Air Combat Evolution, AFRL Golden Horde, and Air Force SkyBorg highlight our impact and innovation.

Responsibilities

• 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.

Requirements

• 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.
• Be 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.
• Able to obtain an Interim Secret level security clearance by your start date and can ultimately obtain a TS/SCI level clearance.

Qualifications

• 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.
• Be 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.
• Able to obtain an Interim Secret level security clearance by your start date and can ultimately obtain a TS/SCI level clearance.

Skills

• Experience with advanced RL topics such as multi-agent RL (MARL), inverse RL (IRL), or hierarchical RL (HRL).
• Background in control theory (e.g., Model Predictive Control, optimal control), game theory, or dynamical systems.
• Demonstrated experience with robotics or aerospace simulation platforms (e.g., Gazebo, AirSim, AFSIM, MATLAB/Simulink).
• Demonstrated experience applying advanced data analysis techniques or explainable AI to understand complex system behaviors.
• Contributed to publications or presentations at relevant AI or robotics conferences.
• Hold an active TS/SCI level security clearance.

Benefits

At APL, we offer a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development. We also provide a healthy work/life balance and a vibrant, welcoming atmosphere where you can bring your authentic self to work.

Pay

Minimum Rate: $100,000 Annually
Maximum Rate: $245,000 Annually

Schedule

Full-time position