Machine Learning Engineer

The Machine Learning Engineer role at Johns Hopkins Applied Physics Laboratory (APL) supports the full lifecycle of machine learning algorithm development, from design through evaluation and implementation. The work is focused on national defense non-kinetic systems and the development of advanced AI methods and software pipelines for real-world planning and decision-making.

Location

Laurel, MD (onsite)

Salary

USD 100,000 - 245,000 per yearly

Role Summary

As a Machine Learning Engineer, you will design, implement, and evaluate ML algorithms for challenging problems in planning, perception, coordination, and control. The position includes building and integrating intelligent decision-making capabilities, leveraging cutting-edge AI approaches and development pipelines.

Responsibilities

• Design, implement, and evaluate advanced machine learning algorithms to address challenging real-world planning, perception, coordination, and control problems for national defense.
• Develop software pipelines that connect data streams, simulation environments, and intelligent decision-making algorithms.
• Apply technologies and concepts at the forefront of AI, 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 the group and with partner teams across APL.
• Engage directly with sponsors to communicate proposed concepts, solutions, and analysis.

Requirements

• Have a Bachelor’s degree in Mathematics, Physics, Engineering, Computer Science, or a related field.
• Have at least 2+ years of experience in machine learning and data science fields.
• Have at least one year of hands-on experience applying or developing machine learning algorithms using common libraries such as PyTorch or TensorFlow.
• Have strong foundational knowledge in at least two of the following: classification, clustering, deep learning, reinforcement learning, computer vision (object detection and visual tracking), multi-agent systems, or optimization/control theory.
• Demonstrated experience working with version control software such as Git.
• Strong communication skills, both verbal and written.
• Be able to obtain an Interim Secret security clearance by your start date and ultimately obtain a Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet requirements for access to classified information, including U.S. citizenship.

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

Preferred Qualifications

You will go above and beyond minimum requirements if you have one or more of the following:

• An 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 technology areas including multi-agent reinforcement learning, geometric deep learning, multi-modal sensor fusion, or agentic AI.
• A track record of writing deployable, production-level code in Python and/or C/C++ for real-world applications.