Applied Machine Learning Platform Engineer

About this role

About Us
Buzz is revolutionizing the analytics and maintenance of power grid infrastructure through our advanced AI solutions. Our computer vision systems analyze critical infrastructure to enhance safety, reliability, and operational efficiency across the power grid network.
Job Description
We're 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'll build tooling and infrastructure to help scale our training and data pipelines. You'll work within a team of experienced ML engineers with the autonomy to drive your own projects and the support to keep growing.
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

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 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

* Buzz Solutions does not provide Visa sponsorship for work authorizations in the United States at this time *
Originally posted on Himalayas