Machine Learning Engineer

Machine Learning Engineer role at Johns Hopkins Applied Physics Laboratory (APL) focused on applying modern AI to national defense non-kinetic systems in an onsite setting in Laurel, MD.

Responsibilities

• Design, implement, and evaluate advanced machine learning algorithms for planning, perception, coordination, and control challenges
• Build software pipelines that connect data streams, simulation environments, and intelligent decision-making components
• Apply cutting-edge AI methods including deep reinforcement learning, foundation models, and large language models
• Develop and work with neural network architectures such as convolutional, recurrent, and graph neural networks
• Apply computer vision techniques in support of system objectives
• Use physics-based modeling and simulation tools to support algorithm development and analysis
• Collaborate with scientists and engineers within the group and across APL
• Engage with sponsors to communicate proposed concepts, solutions, and supporting analysis

Requirements

• 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 or developing ML algorithms using common libraries such as PyTorch or TensorFlow
• Strong foundational knowledge in at least two of the following areas: classification, clustering, deep learning, reinforcement learning, computer vision (object detection and visual tracking), multi-agent systems, optimization/control theory
• Demonstrated experience working with version control software such as Git
• Strong verbal and written communication skills
• Ability to obtain an Interim Secret security clearance by the start date and ultimately obtain a Secret clearance; U.S. citizenship required

Technologies

• 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

Minimum and Maximum Salary

• Minimum: USD 100,000 per year
• Maximum: USD 245,000 per year

Preferred Qualifications

• MS in Mathematics, Physics, Engineering, Computer Science, or a related field
• 5+ years of experience designing and implementing AI/ML algorithms across 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 technology areas: multi-agent reinforcement learning, geometric deep learning, multi-modal sensor fusion, agentic AI
• Track record of writing deployable, production-level code in Python and C/C++ for real-world applications