Machine Learning Engineer

We are looking for a highly skilled professional passionate about developing advanced GeoAI capabilities with multimodal deep learning solutions. This opportunity involves using state-of-the-art techniques in computer vision and vision-language models to enhance analytic workflows with geospatial datasets.

Responsibilities:

• Rapidly prototype containerized multimodal deep learning solutions and associated data pipelines.
• Implement cutting-edge computer vision techniques for tasks such as image retrieval, segmentation, AI-assisted labeling, object detection, and visual question answering.
• Work with diverse geospatial datasets including satellite and aerial imagery, full-motion video (FMV), ground photos, and OpenStreetMap.

Qualifications:

• Security Clearance Required: Active TS (SCI eligibility) clearance and eligibility to obtain a CI poly is required upon application for initial consideration. **This position is not open for any clearance upgrades or sponsorship.**
• Education/Experience:
  • Bachelor's or Master's Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or equivalent experience.
  • 5+ years of professional experience.
• Required Skills:
  • Proficiency in applying transfer learning and knowledge distillation to fine-tune pre-trained models for segmentation and object detection using satellite imagery.
  • Experience in building secure containerized Python applications and automating builds with CI/CD pipelines.
  • Experience querying and retrieving imagery using S3 compliant APIs and preprocessing images with libraries like Boto3 and NumPy.
  • Proficiency with deep learning frameworks such as PyTorch or TensorFlow for optimizing convolutional neural networks (CNN) like ResNet or U-Net.
  • Experience leveraging CUDA for GPU accelerated computing.
  • Demonstrated experience with version control systems, such as GitLab.

Preferred Skills:

• Experience with the HuggingFace Transformers library and hub.
• Experience with OpenShift and Kubernetes container orchestration.
• Working knowledge of Vision Transformers (ViT) like DINO or DeiT.
• Experience in communicating methodological choices and model results.
• Experience with verification and validation test benches.
• Experience with Explainable AI (XAI) techniques and Open Neural Net Exchange (ONNX).
• Intelligence Community experience is preferred.