Senior Computer Vision & Machine Learning Engineer

About the role

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