Applied Machine Learning Platform Engineer

About the Company

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.

Responsibilities

• Design, build, and maintain scalable training infrastructure for computer vision workloads
• Implement and manage distributed training pipelines (multi-GPU, multi-node)
• Build and maintain robust data pipelines for ML development
• Design database schemas and storage strategies for managing large training datasets, annotations, and model artifacts
• Implement and manage feature stores, data versioning, and experiment tracking
• Automate existing analysis workflows
• Maintain clear documentation for platform components, data contracts, and deployment processes
• Communicate infrastructure decisions and tradeoffs to ML engineers and stakeholders
• Conduct thorough code reviews and write integration tests for ML pipelines

Requirements

• 2-4 years of industry experience in platform, backend, data, or MLOps engineering roles
• Proficiency in Python (idiomatic code, type hints, async patterns, packaging)
• Strong software engineering fundamentals including testing, API design, and system design
• Hands-on experience building and operating distributed cloud machine learning infrastructure
• Experience with database design and data systems for ML workloads
• Proficiency with Python ecosystem (Pytest, FastAPI, Pydantic) and tooling (Git, Docker, UV)
• Experience with ML tracking (MLflow, Weights & Biases) and automation (GitHub Actions, CI/CD, Prefect)
• Experience with infrastructure (AWS, GCP, Kubernetes, Helm, Terraform) and databases (Postgres, DynamoDB, Bigtable)