Computer Vision & Machine Learning Engineer

Buzz Solutions

About The Position

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 join our computer vision team 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.

Requirements

2-4 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
Demonstrated ability to read ML research papers, extract key ideas, and implement them
Experience adapting published methods to specific use cases and validating against baselines
Experience selecting, fine-tuning, and adapting model architectures (CNNs, transformers, foundation models) for specific use cases
Ability to debug training instabilities and conduct systematic error analysis
Proficiency in Python and core ML libraries: PyTorch and Lightning, OpenCV, NumPy and pandas, Scikit-Learn
Strong software engineering practices: Git version control, Unit and integration testing (Pytest), CI/CD pipelines (GitHub Actions), Experiment tracking and model versioning, Docker and reproducible environments, Python type hinting

Responsibilities

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
Own and deliver end-to-end computer vision projects focused on: Equipment defect detection, Thermal anomaly identification, Vegetation encroachment monitoring
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
Design and implement data pipelines including ingestion, preprocessing, annotation workflows, and quality monitoring
Experiment tracking and model versioning (configurations, random seeds, dataset versions, environment specs, and model checkpoints)
Build model serving pipelines that meet latency and throughput requirements
Conduct thorough code reviews and write integration tests for ML pipelines
Communicate research findings, technical decisions, and model limitations clearly to stakeholders