Machine Learning Engineer

Join the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, MD (onsite) as a Machine Learning Engineer contributing across the full lifecycle of ML algorithm development and implementation. This role supports national defense non-kinetic systems, where you will apply modern AI methods alongside real development pipelines, simulation environments, and intelligent decision-making systems. Compensation: $100,000 to $245,000 per year. Experience: at least 2 years. Education: Bachelor's degree in Mathematics, Physics, Engineering, Computer Science, or a related field. What you'll do Design, implement, and evaluate advanced machine learning algorithms to address real-world planning, perception, coordination, and control challenges for national defense. Develop software pipelines that connect data streams , simulation environments , and intelligent decision-making algorithms . Apply cutting-edge AI technologies and concepts, including deep reinforcement learning, foundation models, large language models, convolutional/recurrent/graph neural networks, computer vision, and physics-based modeling and simulation tools. Collaborate with scientists and engineers within your group and across APL to advance solutions. Engage with sponsors to communicate proposed concepts, solutions, and analysis. What you'll bring Bachelor's degree in Mathematics, Physics, Engineering, Computer Science, or a related field. At least 2+ years of experience in machine learning and data science. At least 1 year of hands-on experience applying and/or developing ML algorithms using common libraries such as PyTorch or TensorFlow . Strong foundational knowledge in at least two areas: classification, clustering, deep learning, reinforcement learning, computer vision (object detection and visual tracking), multi-agent systems, or optimization/control theory. Demonstrated experience using version control software such as Git . Strong communication skills, both verbal and written. Ability to obtain an Interim Secret clearance by your start date and ultimately a Secret clearance. If selected, you will be subject to a government security clearance investigation and must meet eligibility requirements, including U.S. citizenship . Technologies you'll work with PyTorch , TensorFlow , Git Deep reinforcement learning, foundation models, large language models Convolutional neural networks, recurrent neural networks, graph neural networks Computer vision Physics-based modeling and simulation tools Nice to have MS in Mathematics, Physics, Engineering, Computer Science, or a related field. 5+ years of experience designing and implementing AI/ML algorithms for a variety of datasets. Proven experience applying state-of-the-art deep learning techniques to solve distributed resource allocation problems. Hands-on experience building computer vision pipelines for detection, tracking, segmentation, or multi-modal sensor fusion. Experience with modeling and simulation platforms such as AFSIM, Blender, Unity, or Unreal. Comfort working in high performance computing environments (GPU/CPU clusters). Proficiency in one or more: multi-agent reinforcement learning, geometric deep learning, multi-modal sensor fusion, agentic AI. Track record of writing deployable, production-level code (Python, C/C++) for real-world applications. #J-18808-Ljbffr Johns Hopkins Applied Physics Laboratory (APL)