Job Description:
• Own and deliver end-to-end computer vision projects for equipment defect detection, thermal anomaly identification, vegetation encroachment monitoring, and surveillance of closed areas for human and animal intrusion
• Scope, plan, and execute projects from problem framing through production deployment and monitoring
• Deliver client projects by translating client requirements and raw data into working computer vision solutions
• Contribute to shared team projects and coordinate with engineers to deliver common milestones
• Stay current with ML/CV research and evaluate promising methods for the power-grid domain
• Adapt and implement algorithms from research papers, validate against baselines, and benchmark production viability
• Apply advances in deep learning and generative AI to model training, accuracy, and reliability
• Design experiments with hyperparameter tuning, ablation studies, and appropriate baselines
• Perform structured error analysis across failure modes and data slices
• 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 for ingestion, preprocessing, annotation workflows, and quality monitoring
• Own experiment tracking and model versioning
• Build model-serving pipelines meeting latency and throughput requirements
• Conduct code reviews and write integration tests for ML pipelines
• Share knowledge, uphold ML software-quality standards, and communicate research findings, technical decisions, and model limitations to stakeholders and clients
Requirements:
• 2-5 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, vision language models, and similarity search
• Experience taking at least one ML model into production and maintaining it there
• Experience selecting, fine-tuning, and adapting CNN, transformer, and foundation model architectures for specific use cases
• Proven ability to read ML research papers, extract key ideas, and implement them
• Ability to debug training instabilities and conduct systematic error analysis
• Proficiency in Python and the core ML stack: PyTorch, Lightning, OpenCV, NumPy, pandas, Scikit-Learn, FastAPI, and Pydantic
• Strong software engineering practices, including Git, unit and integration testing with Pytest, CI/CD pipelines with GitHub Actions, Docker, reproducible environments, experiment tracking, model versioning, ML DevOps, and Python type hinting
• Proven ability to own technical projects independently from problem framing through production deployment
• United States work authorization; the position does not include sponsorship
Benefits: