Machine Learning Engineer

Summary

The job is for a Machine Learning Engineer at INflow Federal, focusing on AI/ML solutions for the Department of Defense. The role involves rapid prototyping and implementing State-of-the-Art Computer Vision and Vision Language Models using satellite imagery, full-motion video, ground photos, and OpenStreetMap.

Requirements

• 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 to include hardening, scanning, automating builds using CI/CD pipelines
• Demonstrated professional or academic experience using Python to query and retrieve imagery from S3 compliant API's 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
• Bachelors or Masters Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or equivalent experience in lieu of degree
• 2+ years experience
• Active DoD TS/SCI

Responsibilities

Rapid prototype containerized multimodal deep learning solutions and associated data pipelines to enable GeoAI capabilities for improving analytic workflows and addressing key intelligence questions

Preferred Qualifications

• 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)