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.
Requirements & Skills:
Bachelor’s or Master’s Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or equivalent experience in lieu of degree.
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 professional or academic experience building secure containerized Python applications including hardening, scanning, and automating builds using CI/CD pipelines.
Demonstrated professional or academic experience using Python to query and retrieve imagery from S3-compliant API and perform common image preprocessing such as chipping, augment, or conversion using common libraries like Boto3 and NumPy.
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.
Demonstrated professional or academic experience with version control systems such as GitLab.
Demonstrated experience leveraging CUDA for GPU-accelerated computing.
Demonstrated professional or academic experience with the HuggingFace Transformers library and hub.
Demonstrated experience with OpenShift and container orchestration within Kubernetes using Helm, Kubectl, Kustomize, or Operators.
Demonstrated experience with Vision Transformers (ViT) such as DINO or DeiT.
Demonstrated academic or professional experience communicating methodological choices and model results.
Demonstrated experience with verification and validation test benches.
Demonstrated experience with Explainable AI (XAI) techniques.
Demonstrated experience with Open Neural Net Exchange (ONNX).
