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 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.