Job description

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! Who are we? 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 SkyBorg) 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! As a Reinforcement Learning Engineer for Autonomous Systems, 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. Qualifications 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, 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. About Us Why Work at APL? 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 challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates. At APL, we celebrate our differences of perspectives and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL’s campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities at jhuapl.edu . All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law. APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contact Accessibility@jhuapl.edu . The referenced pay range is based on JHU APL’s good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level with consideration for internal parity. For salaried employees scheduled to work less than 40 hours per week, annual salary will be prorated based on the number of hours worked. APL may offer bonuses or other forms of compensation per internal policy and/or contractual designation. Additional compensation may be provided in the form of a sign-on bonus, relocation benefits, locality allowance or discretionary payments for exceptional performance. APL provides eligible staff with 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. Applications are accepted on a rolling basis. Minimum Rate $100,000 Annually Maximum Rate $245,000 Annually

Requirements

Must-haves

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!
As a Reinforcement Learning Engineer for Autonomous Systems, 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, TensorFlow).
Have a solid understanding of the mathematical foundations of ML, including probability, statistics, and linear algebra.
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.

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aimachine learningpythonmlgoaccessibility

Platform: jibe
Effort: medium

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