Applied Machine Learning Platform Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, develop, and sustain scalable training infrastructure tailored for computer vision applications.

• Implement and oversee distributed training pipelines (multi-GPU, multi-node) to facilitate extensive model training and hyperparameter optimization.

• Construct and uphold efficient data pipelines for machine learning development.

• Create database schemas and storage solutions for managing substantial training datasets, annotations, and model artifacts.

• Establish and maintain feature stores, data versioning, and experiment tracking to promote dependable model iteration.

• Automate current analysis workflows.

• Ensure comprehensive documentation of platform components, data contracts, and deployment procedures.

• Clearly communicate infrastructure decisions, tradeoffs, and system constraints to machine learning engineers and stakeholders.

• Perform thorough code reviews and develop integration tests for machine learning pipelines.

⛳️ Requirements

• 2-4 years of professional experience in platform, backend, data, or MLOps engineering roles.

• Proficiency in Python — including idiomatic coding, type hints, asynchronous patterns, packaging, and performance-oriented implementation.

• Strong grounding in software engineering principles — testing, code review, API design, and component-level system architecture.

• Practical experience in constructing and managing distributed cloud machine learning infrastructure.

• Expertise in designing and maintaining scalable training infrastructure, ensuring ML platform reliability, and optimizing data pipelines for high throughput.

• Familiarity with database design and data systems tailored for ML workloads — encompassing schema design, query optimization, and storage solutions for large datasets.

• Exceptional skills in workflow orchestration and automation.

• Solid proficiency in Python and essential ML tools:

• Python ecosystem: Pytest, UV, FastAPI, Pydantic.

• Tooling: Git, Docker, UV.

• Tracking: MLflow, Weights & Biases, or similar.

• Automation: GitHub Actions, CI/CD, Prefect or equivalent.

• Infrastructure: AWS, GCP, Kubernetes, Helm, Terraform or similar.

• Databases: PostgreSQL, DynamoDB, Bigtable.

🏝️ Benefits

• Buzz Solutions does not offer Visa sponsorship for work authorizations in the United States at this time.