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Machine Learning Infra Engineer

Reducto· ML/Platform
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San Francisco, CAFull-timePosted 5mo ago

About the role

Role focused on building the inference and training frameworks to enable scalable ML at Reducto, collaborating with ML and Platform teams to scale training, improve serving performance, and implement observability across the stack.

Responsibilities

  • Build, and maintain our training and inference stack with an emphasis for fast iteration on training + flexibility for exploring new methods and high performance in inference.
  • Develop benchmarks for both sets of stacks to identify bottlenecks.
  • Explore SOTA advances in training and inference and work to apply them.
  • Design systems for scaling model training across multi-node, multi-GPU environments with strong reliability and observability.
  • Scale distributed training and inference workloads across large GPU clusters while improving utilization, reliability, and cost efficiency.
  • Build the tooling, abstractions, and observability that help ML engineers move faster from experiment to production.

Qualifications

  • Have strong Python skills + a background in systems engineering.
  • Are comfortable with Kubernetes and distributed training frameworks.
  • Love getting your hands dirty with real-world implementation challenges.
  • Operate well in fast-changing, high-growth environments.
  • Collaborate effectively across technical and non-technical teams.
  • Take full ownership from strategy through execution.
  • Have 3+ years of experience.

Skills

Required:PythonSystems engineeringKubernetes

Benefits

  • Unlimited PTO
  • Lunch: Free daily lunch at the office
  • Reimbursed Transportation
  • Insurance: health, dental, and vision
  • Health and Wellness Budget: up to $150/mo
  • Parental Leave
Last updated Sep 4, 2026. Interested? Apply on the company site.
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About Reducto

The most accurate API to parse documents

Reducto offers robust and reliable document ingestion for any workflow. Our API allows you to convert complex, unstructured documents into structured outputs that are perfect for RAG, process automation, and more.

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