Job Details

Deliver simple solutions to complex problems as a Machine Learning Engineer at NDSI. 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 NDSI, 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 Engineers joining our team to support our intelligence customer in St. Louis, MO.

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 conducting 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:

1. Education: Bachelor or Master's Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or equivalent experience in lieu of degree.
2. Experience: 5+ years

Technical skills:

1. 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.
2. Demonstrated professional or academic experience building secure containerized Python applications to include hardening, scanning, automating builds using CI/CD pipelines.
3. Demonstrated professional or academic experience using Python to query and retrieve imagery from S3 compliant APIs and perform common image preprocessing such as chipping, augmenting, or conversion using common libraries like Boto3 and NumPy.
4. Demonstrated professional or academic experience with deep learning frameworks such as PyTorch or TensorFlow to optimize convolutional neural networks (CNN) such as ResNet or U-Net for object detection or segmentation tasks using satellite imagery.
5. Demonstrated professional or academic experience with version control systems such as GitLab.
6. Demonstrated experience leveraging CUDA for GPU accelerated computing.

Skills and abilities desired:

1. Demonstrated professional or academic experience with the HuggingFace Transformers library and hub.
2. Demonstrated experience with OpenShift and container orchestration within Kubernetes using Helm, Kubectl, Kustomize, or Operators.
3. Demonstrated experience with Vision Transformers (ViT) such as DINO or DeiT.
4. Demonstrated academic or professional experience communicating methodological choices and model results.
5. Demonstrated experience with verification and validation test benches.
6. Demonstrated experience with Explainable AI (XAI) techniques.
7. Demonstrated experience with Open Neural Net Exchange (ONNX).