Applied Machine Learning Platform Engineer

Buzz Solutions

4h

About The Position

Buzz is revolutionizing the analytics and maintenance of power grid infrastructure through advanced AI solutions. Our computer vision systems analyze critical infrastructure to enhance safety, reliability, and operational efficiency across the power grid network. We are looking for an entry/mid-level Applied Machine Learning Platform Engineer to join our computer vision team and help improve the databases, cloud infrastructure, and tooling our team builds on. You will build tooling and infrastructure to help scale our training and data pipelines, working within a team of experienced ML engineers with the autonomy to drive your own projects and the support to keep growing.

Requirements

• 2-4 years of industry experience in platform, backend, data, or MLOps engineering roles
• Python proficiency — idiomatic code, type hints, async patterns, packaging, and performance-aware implementation
• Strong software engineering fundamentals — testing, code review, API design, component-level system design
• Hands-on experience building and operating distributed cloud machine learning infrastructure
• Designing and maintaining scalable training infrastructure, managing ML platform reliability, optimizing data pipelines for throughput at scale
• Experience with database design and data systems for ML workloads — schema design, query optimization, and storage strategies for large-scale datasets
• Excels at workflow orchestration and automation
• Solid proficiency in Python and core ML tooling: Python ecosystem: Pytest, UV, FastAPI, Pydantic
• Solid proficiency in core ML tooling: Tooling: Git, Docker, UV
• Solid proficiency in core ML tooling: Tracking: MLflow, Weights & Biases, or equivalent
• Solid proficiency in core ML tooling: Automation: Github Actions, CI/CD, Prefect or equivalent
• Solid proficiency in core ML tooling: Infrastructure: AWS, GCP, Kubernetes, Helm, Terraform or equivalent
• Solid proficiency in core ML tooling: Databases: postgres, DynamoDB, Bigtable

Responsibilities

• Design, build, and maintain scalable training infrastructure for computer vision workloads
• Implement and manage distributed training pipelines (multi-GPU, multi-node) to support large-scale model training and hyperparameter tuning
• 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 to support reliable model iteration
• Automate existing analysis workflows
• Maintain clear documentation for platform components, data contracts, and deployment processes
• Communicate infrastructure decisions, tradeoffs, and system limitations clearly to ML engineers and stakeholders
• Conduct thorough code reviews and write integration tests for ML pipelines

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed
• Number of Employees: 1-10 employees