Machine Learning Engineer

Johns Hopkins Applied Physics Laboratory provided pay range

This range is provided by Johns Hopkins Applied Physics Laboratory. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

$/yr - $/yr

Do you have demonstrated machine learning experience and want to apply that experience to solving a wide variety of complex problems in this rapidly evolving field? Do you thrive in a collaborative research environment, working alongside an energetic, multidisciplinary team of scientists and engineers? Are you ready to help the US secure and maintain leadership in the development and deployment of AI/ML algorithms for non‑kinetic defense systems?

If so, we’re looking for someone like you to join our team at APL! We are seeking an experienced Machine Learning Engineer who will contribute to all phases of the machine learning algorithm development and implementation. You will be joining a team of engineers and scientists who are at the forefront of APL’s mission to provide innovative solutions to critical challenges.

• Design, implement, and evaluate advanced machine learning algorithms to solve challenging real‑world planning, perception, coordination, and control problems in support of national defense.
• Develop software pipelines to integrate data streams, simulation environments, and intelligent decision‑making algorithms.
• Work with technologies and concepts at the cutting edge of AI, including but not limited to: deep reinforcement learning, foundation models, large language models, convolutional/recurrent/graph neural networks, computer vision, and physics‑based modeling and simulation tools.
• Collaborate closely with the talented team of scientists and engineers in our group and with others across APL.
• Engage directly with sponsors to communicate proposed concepts, solutions, and analysis.

You meet the minimum requirements for the job if you...

• 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/developing machine learning algorithms using common libraries such as PyTorch or Tensor Flow.
• 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.
• Have demonstrated experience in working with version control software like Git.
• Have strong, effective communication skills both verbal and written.
• Are able to obtain an Interim Secret level security clearance by your start date and can ultimately obtain a Secret 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.

• Have an MS in Mathematics, Physics, Engineering, Computer Science, or a related field.
• Have 5+ years of experience in designing and implementing AI/ML algorithms for a variety of datasets.
• Have proven experience applying state‑of‑the‑art deep learning techniques to solve distributed resource allocation problems.
• Have hands‑on experience building computer vision pipelines for detection, tracking, segmentation, or multi‑modal sensor fusion.
• Have experience with modeling and simulation platforms such as AFSIM, Blender, Unity, or Unreal.
• Are comfortable working in high performance computing environments (GPU/CPU clusters).
• Have proficiency in one or more of the following technology areas: multi‑agent reinforcement learning, geometric deep learning, multi‑modal sensor fusion, agentic AI.
• Have a track record of writing deployable, production‑level code (Python, C/C++) for real‑world applications.

The Johns Hopkins University Applied Physics Laboratory (APL) brings world‑class expertise to our nation’s most critical defense, security, space and science…