Senior Computer Vision & Machine Learning Engineer

at  Buzz Solutions

JOB DESCRIPTION

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 seeking an experienced Machine Learning Engineer to help lead our computer vision initiatives. You’ll drive the development of cutting-edge models for power grid analysis and provide a leadership anchor on a team of talented ML engineers.

QUALIFICATIONS & EXPERIENCE

• 7-10 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, Similarity search, Vision language models
• Proven track record of deploying and maintaining ML models in production
• Expert proficiency in PyTorch, Lightning, OpenCV, and Scikit-Learn
• Proficiency in FastAPI and Pydantic
• Strong software engineering foundation including: Git version control, Test driven development (Pytest), CI/CD, ML devops, Python type hinting
• 2-3 years of proven leadership experience of technical teams

DESIRED ADDITIONAL EXPERIENCE

• 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

• Architect and lead end-to-end computer vision projects focused on: Equipment defect detection, Thermal anomaly identification, Vegetation encroachment monitoring, Surveillance of closed areas for human and animal intrusions
• Drive innovation by incorporating the latest advances in deep learning and generative AI to enhance model training, accuracy and reliability
• Develop production-grade Python libraries for the complete ML lifecycle
• Mentor team members and establish best practices for model development, evaluation, deployment, and monitoring
• Advocate for and uphold software quality standards within the ML team