Defense ML Engineer: Advanced AI/ML & Systems

Talentify; Laurel (MD); On-site; USD 100,000 - 245,000; Full time

Job summary

Johns Hopkins University Applied Physics Laboratory (APL) in Laurel, MD, seeks an experienced Machine Learning Engineer to contribute to all phases of ML algorithm development and implementation. You will join a multidisciplinary team of scientists and engineers tackling national defense challenges with cutting-edge AI technologies.

You will design, implement, and evaluate advanced ML algorithms, develop data pipelines, and collaborate with sponsors to communicate concepts and results.

Qualifications

• Have 2+ years of experience in machine learning and data science.
• Bachelor's degree in Mathematics, Physics, Engineering, Computer Science, or related field.
• Hands-on ML with PyTorch or TensorFlow.
• Strong Git version control experience.
• Excellent communication and collaboration skills.
• Eligible for Interim Secret security clearance; U.S. citizenship required.

Responsibilities

• Design, implement, and evaluate advanced ML algorithms for defense-related problems.
• Develop software pipelines to integrate data streams, simulation environments, and decision-making.
• Work with cutting-edge AI technologies (foundation models, DL/vision/VL).
• Collaborate with scientists, engineers, and sponsors to communicate concepts and results.
• Engage in multiple phases of ML algorithm development and deployment.

Skills

Machine learning

Python

Git

Communication

Teamwork

Education

Bachelor's degree in Mathematics/Physics/Engineering/CS

MS preferred

Tools

PyTorch

TensorFlow

CUDA

Job description

Johns Hopkins University Applied Physics Laboratory (APL) in Laurel, MD, seeks an experienced Machine Learning Engineer to contribute to all phases of ML algorithm development and implementation. You will join a multidisciplinary team of scientists and engineers tackling national defense challenges with cutting-edge AI technologies.

You will design, implement, and evaluate advanced ML algorithms, develop data pipelines, and collaborate with sponsors to communicate concepts and results.