{"schemaVersion":"jobsearcher.job.v1","id":"7cfc4f22a4f1237a0055a533","url":"https://jobsearcher.com/jobs/7cfc4f22a4f1237a0055a533","canonicalUrl":"https://jobsearcher.com/jobs/7cfc4f22a4f1237a0055a533","title":"Applied Machine Learning Platform Engineer","description":"About UsBuzz 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.ResponsibilitiesDesign, build, and maintain scalable training infrastructure for computer vision workloadsImplement and manage distributed training pipelines (multi-GPU, multi-node) to support large-scale model training and hyperparameter tuningBuild and maintain robust data pipelines for ML developmentDesign database schemas and storage strategies for managing large training datasets, annotations, and model artifactsImplement and manage feature stores, data versioning, and experiment tracking to support reliable model iterationAutomate existing analysis workflowsMaintain clear documentation for platform components, data contracts, and deployment processesCommunicate infrastructure decisions, tradeoffs, and system limitations clearly to ML engineers and stakeholdersConduct thorough code reviews and write integration tests for ML pipelinesQualifications & Experience2-4 years of industry experience in platform, backend, data, or MLOps engineering rolesPython proficiency — idiomatic code, type hints, async patterns, packaging, and performance-aware implementationStrong software engineering fundamentals — testing, code review, API design, component-level system designHands-on experience building and operating distributed cloud machine learning infrastructureDesigning and maintaining scalable training infrastructure, managing ML platform reliability, optimizing data pipelines for throughput at scaleExperience with database design and data systems for ML workloads — schema design, query optimization, and storage strategies for large-scale datasetsExcels at workflow orchestration and automationSolid proficiency in Python and core ML tooling:Python ecosystem: Pytest, UV, FastAPI, PydanticTooling: Git, Docker, UVTracking: MLflow, Weights & Biases, or equivalentAutomation: Github Actions, CI/CD, Prefect or equivalentInfrastructure: AWS, GCP, Kubernetes, Helm, Terraform or equivalentDatabases: postgres, DynamoDB, Bigtable* Buzz Solutions does not provide Visa sponsorship for work authorizations in the United States at this time *","company":"Buzz Solutions","rawCompany":"buzz solutions","city":"Denver","state":"CO","isRemote":false,"isActive":false,"createdAt":"2026-08-14T12:39:34.736Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Applied Machine Learning Platform Engineer","description":"About UsBuzz 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.ResponsibilitiesDesign, build, and maintain scalable training infrastructure for computer vision workloadsImplement and manage distributed training pipelines (multi-GPU, multi-node) to support large-scale model training and hyperparameter tuningBuild and maintain robust data pipelines for ML developmentDesign database schemas and storage strategies for managing large training datasets, annotations, and model artifactsImplement and manage feature stores, data versioning, and experiment tracking to support reliable model iterationAutomate existing analysis workflowsMaintain clear documentation for platform components, data contracts, and deployment processesCommunicate infrastructure decisions, tradeoffs, and system limitations clearly to ML engineers and stakeholdersConduct thorough code reviews and write integration tests for ML pipelinesQualifications & Experience2-4 years of industry experience in platform, backend, data, or MLOps engineering rolesPython proficiency — idiomatic code, type hints, async patterns, packaging, and performance-aware implementationStrong software engineering fundamentals — testing, code review, API design, component-level system designHands-on experience building and operating distributed cloud machine learning infrastructureDesigning and maintaining scalable training infrastructure, managing ML platform reliability, optimizing data pipelines for throughput at scaleExperience with database design and data systems for ML workloads — schema design, query optimization, and storage strategies for large-scale datasetsExcels at workflow orchestration and automationSolid proficiency in Python and core ML tooling:Python ecosystem: Pytest, UV, FastAPI, PydanticTooling: Git, Docker, UVTracking: MLflow, Weights & Biases, or equivalentAutomation: Github Actions, CI/CD, Prefect or equivalentInfrastructure: AWS, GCP, Kubernetes, Helm, Terraform or equivalentDatabases: postgres, DynamoDB, Bigtable* Buzz Solutions does not provide Visa sponsorship for work authorizations in the United States at this time *","datePosted":"2026-08-14T12:39:34.736Z","dateModified":"2026-08-14T12:39:34.736Z","hiringOrganization":{"@type":"Organization","name":"Buzz Solutions","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Denver","addressRegion":"CO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"7cfc4f22a4f1237a0055a533"},"url":"https://jobsearcher.com/jobs/7cfc4f22a4f1237a0055a533"}}