Applied Machine Learning Platform Engineer

2 months ago

Remote, WorldwideEntry Level / Mid Level

H1B Sponsor

Responsibilities

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

2-4 years of industry experience in platform, backend, data, or MLOps engineering roles.
Proficiency in Python, including idiomatic code and performance-aware implementation.
Strong software engineering fundamentals, including testing and API design.
Hands-on experience building and operating distributed cloud machine learning infrastructure.
Experience with database design and data systems for ML workloads.
Excels at workflow orchestration and automation.
Solid proficiency in Python and core ML tooling, including Pytest and Docker.
Familiarity with cloud infrastructure such as AWS or GCP.