Computer Vision & Machine Learning Engineer

📋 Job Overview

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.

🏢 About Buzz Solutions

Buzz Solutions is a company focused on using AI to improve power grid infrastructure. They develop computer vision systems for analyzing critical infrastructure to enhance safety, reliability, and operational efficiency.

🎯 The Role

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.

✅ Key Responsibilities

• Own and deliver end-to-end computer vision projects focused on equipment defect detection, thermal anomaly identification, vegetation encroachment monitoring, and surveillance of closed areas for human and animal intrusion.
• Scope, plan, and execute your own projects from problem framing through production deployment and monitoring.
• Deliver on client projects, translating client requirements and raw data into working computer vision solutions.
• Contribute to shared team projects, coordinating with other engineers to deliver against common milestones.
• Stay current with ML/CV research, identify promising methods, and evaluate their applicability to our domain.
• Adapt and implement algorithms from papers, validating against baselines and benchmarking for production viability.
• Bring the latest advances in deep learning and generative AI to bear on model training, accuracy, and reliability.
• Design and execute experiments with systematic hyperparameter tuning, ablation studies, and appropriate baselines.
• Perform structured error analysis: categorize failure modes (false positives, missed detections, localization errors, misclassifications) and break down performance by data slices (object size, occlusion, image quality).
• Select and justify model architectures based on task requirements, latency, and accuracy tradeoffs.

📌 Required Qualifications

• 2-5 years of industry experience in computer vision and machine learning.
• Solid understanding of modern computer vision and deep neural networks, including object detection, semantic segmentation, image classification, vision transformers and foundation models, vision language models, and similarity search.
• Experience taking at least one ML model into production and maintaining it there.
• Experience selecting, fine-tuning, and adapting model architectures (CNNs, transformers, foundation models) for specific use cases.
• Demonstrated ability to read ML research papers, extract 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.
• Software engineering practices, including Git version control, unit and integration testing (pytest), CI/CD pipelines (GitHub Actions), Docker and reproducible environments, ML tracking and model versioning, ML DevOps, and Python type hinting.
• Proven ability to own technical projects independently, from problem framing through production deployment.

⭐ Desirable Experience

• Multi-modal computer vision
• Custom object detection model development
• Generative models for data augmentation
• Extracting measurements from GIS and/or drone-metadata-enriched imagery
• Model quantization and latency optimization for edge deployment
• Systematic hyperparameter tuning at scale
• Energy, utility, geospatial, or power grid domains

🎁 Benefits

• This position does not include sponsorship.

🛂 Visa & Eligibility

• This position does not include sponsorship.