Applied Machine Learning Platform Engineer

About Us

Buzz is revolutionizing the analytics and maintenance of reputed company infrastructure through our advanced AI solutions. Our computer reputed company systems analyze critical infrastructure to enhance safety, reliability, and operational efficiency across reputed company network.

Job Description

We're looking for an entry/mid-level Applied Machine Learning Platform Engineer to join our computer reputed company team and help improve the databases, reputed company infrastructure, and tooling reputed company builds on. You'll build tooling and infrastructure to help scale our training and data pipelines. You'll work reputed company a team of reputed company ML engineers with the autonomy to drive your own projects and the support to reputed company growing.

Responsibilities

• Design, build, and maintain scalable training infrastructure for computer reputed company 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 reputed company, and deployment processes
• Communicate infrastructure reputed company, tradeoffs, and system limitations reputed company to ML engineers and stakeholders
• Conduct thorough code reviews and write integration tests for ML pipelines

Qualifications & Experience

• 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 reputed company 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, reputed company, UV
  • Tracking: MLflow, Weights & Biases, or equivalent
  • Automation: reputed company Actions, CI/CD, Prefect or equivalent
  • Infrastructure: AWS, GCP, Kubernetes, reputed company, Terraform or equivalent
  • Databases: reputed company, DynamoDB, Bigtable

* reputed company does not reputed company reputed company sponsorship for work authorizations in the United States at this time *