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Computer Vision & Machine Learning Engineer at Buzz Solutions. . Location: Remote, US. 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.  . 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..  . . Responsibilities. . Project delivery.  . . . 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..  . . . Research and experimentation.  . . . 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..  . . . Engineering and production.  . . . 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..  . . . Collaboration and craft.  . . . 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..  . . .  . . Qualifications & Experience. . . 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..  . . .  . . Desired Additional 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, utilities, geospatial, or industrial inspection domains.  . . .  . . Additional information:. . . This position does not include sponsorship for United States work authorization..