Machine Learning Engineer

Type of Requisition:

Regular

Clearance Level Must Currently Possess:

Top Secret/SCI

Clearance Level Must Be Able to Obtain:

Top Secret SCI + Polygraph

Public Trust/Other Required:

None

Job Family:

Systems Engineering

Job Qualifications:

Skills:

Graphics Processing Units (GPUs), Kubernetes, Tensorflow

Certifications:

None

Experience:

5 + years of related experience

US Citizenship Required:

Yes

Job Description:

Deliver simple solutions to complex problems as a Machine Learning Engineer at GDIT. Here, you'll tailor cutting-edge solutions to the unique requirements of our clients. With a career in application development, you'll make the end user's experience your priority and we'll make your career growth ours.

At GDIT, people are our differentiator. As a Machine Learning Engineer you will help ensure today is safe and tomorrow is smarter. Our work depends on TS/SCI cleared Machine Learning Engineer joining our team to support our intelligence customer in St. Louis, MO or Springfield, VA.

HOW A MACHINE LEARNING ENGINEER  WILL MAKE AN IMPACT

Own your opportunity to serve as a critical component of our nation's safety and security. Make an impact by using your expertise to protect our country from threats.

Job Description

​Rapidly prototype containerized multimodal deep learning solutions and associated data pipelines to enable GeoAI capabilities for improving analytic workflows and addressing key intelligence questions. You will be at the cutting edge of implementing State-of-the-Art (SOTA) Computer Vision (CV) and Vision Language Models (VLM) for scalable, distributed AI workloads in Kubernetes/OpenShift environments. Your work will involve image retrieval, segmentation tasks, AI-assisted labeling, object detection, and visual question answering using geospatial datasets such as satellite and aerial imagery, full-motion video (FMV), ground photos, and OpenStreetMap.

WHAT YOU'LL NEED TO SUCCEED:

Education: Bachelor or Master' Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or equivalent experience in lieu of degree.

Experience: 5+ years

Technical skills:

• Demonstrated experience applying transfer learning and knowledge distillation methodologies to fine-tune pre-trained foundation and computer vision models to quickly perform segmentation and object detection tasks with limited training data using satellite imagery.
• Demonstrated experience building secure, containerized Python applications with Kubernetes-first design, including image hardening, vulnerability scanning, and automated deployments using CI/CD pipelines.
• Demonstrated experience deploying GPU-accelerated deep learning workloads in Kubernetes using NVIDIA TensorRT or CUDA-enabled environments.
• Demonstrated experience implementing and managing Kubernetes/OpenShift-based AI/ML workloads using Kubeflow, Kserve, Knative, or Mlflow for scalable inference and training.
• Demonstrated experience using Python to query and retrieve imagery from S3-compliant APIs, perform image preprocessing (chipping, augmentation, or conversion) using common libraries like Boto3 and NumPy
• Demonstrated experience with deep learning frameworks such as PyTorch or Tensorflow to optimize convolutional neural networks (ResNet or U-Net) for large-scale geospatial applications.
• Demonstrated experience with version control systems such as Gitlab and managing GitOps workflows for ML model deployments.
• Demonstrated experience managing Kubernetes/OpenShift clusters for ML workflows, including scaling GPU workloads and configuring Operators.

 

Skills