Computer Vision & Machine Learning Engineer

Summary

Buzz is revolutionizing analytics and maintenance of power grid infrastructure through advanced AI and computer vision solutions. The Machine Learning Engineer will advance computer vision and foundational model capabilities by adapting research into reliable production systems for power grid analysis, while owning projects from problem framing through deployment and monitoring.

Responsibilities

• Own and deliver 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 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
• Develop production-grade Python libraries for the complete ML lifecycle
• Design and implement data pipelines including ingestion, preprocessing, annotation workflows, and quality monitoring
• Own 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
• Share knowledge with teammates and contribute to best practices for model development, evaluation, deployment, and monitoring
• Advocate for and uphold software quality standards within the ML team
• Communicate research findings, technical decisions, and model limitations clearly to stakeholders and clients

Skills

• 2-5 years of industry experience in computer vision and machine learning
• Solid understanding 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
• 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 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
• This position does not include sponsorship for United States work authorization
• 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, utilities, geospatial, or industrial inspection domains

Qualifications

Must Haves

• 2-5 years of industry experience in computer vision and machine learning
• Solid understanding 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
• 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 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
• This position does not include sponsorship for United States work authorization

Nice to Haves

• 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, utilities, geospatial, or industrial inspection domains

Benefits

• Autonomy to drive your own projects and support to keep growing