Senior Computer Vision & Machine Learning Engineer

Buzz is revolutionizing the analytics and maintenance of power grid infrastructure through our advanced AI solutions. Our computer vision systems analyze critical infrastructure to enhance safety, reliability, and operational efficiency across the power grid network.

We're looking for a Machine Learning Engineer to advance our computer vision initiatives and help build our foundational model capabilities. You'll bridge the gap between cutting-edge research and production systems, reading papers, adapting novel algorithms, and turning them into reliable, deployed models for power grid analysis. You'll work within a team of experienced ML engineers, with the autonomy to drive your own projects and the support to keep growing. You'll operate with a high degree of autonomy.

• 5–10 years of industry experience in computer vision and machine learning. 

• Deep expertise in modern computer vision and deep neural networks, including:
  • Object detection
  • Semantic segmentation
  • Image classification
  • Vision transformers and foundation models
  • Vision language models
  • Similarity search 

• Proven track record of deploying and maintaining ML models in production. 

• Experience selecting, fine-tuning, and adapting model architectures (CNNs, transformers, foundation models) for specific use cases. 

• Demonstrated ability to read ML research papers, extract the key ideas, and implement them. 

• Ability to debug training instabilities and conduct systematic error analysis. 

• Proficiency in Python and the core ML stack:
  • PyTorch and Lightning
  • OpenCV
  • NumPy and pandas
  • Scikit-Learn
  • FastAPI and Pydantic 

• Strong software engineering practices, including:
  • Git version control 
  • Unit and integration testing (Pytest)
  • CI/CD pipelines (GitHub Actions)
  • Docker and reproducible environments
  • Experiment tracking and model versioning
  • ML DevOps
  • Python type hinting 

• Proven ability to own technical projects independently, from problem framing through production deployment. 

• Multi-modal computer vision 
• Custom object detection model development
• Generative models for data augmentation
• ML deployment on edge devices
• Extracting measurements from GIS and/or drone metadata enriched imagery
• Model quantization
• Systematic hyperparameter tuning

• This position does not include sponsorship for United States work authorization.