[Remote] Senior Computer Vision & Machine Learning Engineer

Note: The job is a remote job and is open to candidates in USA. Buzz Solutions develops AI-powered analytics and maintenance solutions for power grid infrastructure, using computer vision to improve safety, reliability, and operational efficiency. The Senior Computer Vision & Machine Learning Engineer will lead end-to-end computer vision projects, adapt research into production-ready models, build ML pipelines and serving systems, and communicate technical findings to stakeholders and clients.

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

• 5–10 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
• Vision transformers and foundation models
• Vision language models
• Similarity search
• Proven track record of deploying and maintaining ML models in production
• 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
• ML deployment on edge devices
• Extracting measurements from GIS and/or drone metadata enriched imagery
• Model quantization
• Systematic hyperparameter tuning

Benefits

• Support to keep growing
• High degree of autonomy

Company Overview