Machine Learning Engineer

Machine Learning Engineer

Requirements

• Bachelor or Master’ Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or equivalent experience in lieu of degree.
• Experience: 5+ years
• 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 and experience leveraging CUDA for GPU accelerated computing.
• Active TS/SCI w/ Poly

Responsibilities

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

Preferred Qualifications

• Demonstrated professional or academic experience with the HuggingFace Transformers library, hub and 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 and Demonstrated academic or professional experience communicating methodological choices and model results.
• Demonstrated experience with verification, validation test benches; demonstrated experience with Explainable AI (XAI) techniques and experience with Open Neural Net Exchange (ONNX).
• Experience in building custom operators with the operator-sdk and developing Kubernetes Custom Resource Definitions (CRD) or API’s
• Go programming language experience