The job, in the employer's words
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. You'll operate with a high degree of autonomy.
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
- 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.
Desired Additional Experience
- 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
Additional information:
- This position does not include sponsorship for United States work authorization.
Requirements mentioned
- 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.
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