Machine Learning Engineer
Johns Hopkins APL · Laurel, MD · Artificial Intelligence
About this role
Johns Hopkins APL is hiring a mid-level Machine Learning Engineer based in Laurel, MD. The posting calls out experience with Python, C, TensorFlow, PyTorch and roughly 2+ years of relevant work. Listed education preference: a bachelor's degree or equivalent.
- Role
- Machine Learning Engineer
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Location
- Laurel, MD
- Experience
- 2+ years
- Education
- Bachelor's degree
- Visa
- Not sponsored
- Department
- Artificial Intelligence
- Posted
- Jan 6, 2026
More roles at Johns Hopkins APL
Job description
from Johns Hopkins APL careersDo 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.
As a Machine Learning Engineer, you will...
- 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.