Note: The job is a remote job and is open to candidates in USA. Buzz Solutions is revolutionizing the analytics and maintenance of power grid infrastructure through advanced AI solutions. They are seeking a Machine Learning Engineer to join their computer vision team, focusing on building foundational model capabilities and bridging the gap between research and production systems.
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
Skills
- 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
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