Applied Machine Learning Platform Engineer

Buzz Solutions

. Design, build, and maintain scalable training infrastructure for computer vision workloads .

Posted 4/21/2026full-timeRemote • 🇺🇸 United StatesJuniorMid-LevelWebsite

Tech Stack

Tools & technologies

AWSCloudDockerDynamoDBGoogle Cloud PlatformKubernetesNode.jsPostgresPythonTerraform

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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
Tooling: Git, Docker, UV
Tracking: MLflow, Weights & Biases, or equivalent
Automation: Github Actions, CI/CD, Prefect or equivalent
Infrastructure: AWS, GCP, Kubernetes, Helm, Terraform or equivalent
Databases: postgres, DynamoDB, Bigtable

Benefits

Comp & perks

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

ATS Keywords

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Hard Skills & Tools

Pythondistributed training pipelinesdata pipelinesdatabase designfeature storesexperiment trackingcode reviewsintegration testsworkflow orchestrationautomation

Soft Skills

communicationcollaborationproblem-solvingattention to detaildocumentation