Machine Learning Infra Engineer

Reducto

$130K — $180K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years experience in ML infrastructure or related fields
  • Strong Python programming skills
  • Familiarity with Kubernetes and distributed training frameworks
  • Background in systems engineering
  • Proven ability to scale training across multi-node, multi-GPU environments
  • Experience in benchmarking and identifying bottlenecks in ML workflows
  • Capability to design observability frameworks for ML systems

Responsibilities

  • Build and maintain our training and inference stack for fast iterations
  • Develop benchmarks to identify performance bottlenecks
  • Apply state-of-the-art advances in ML training and inference
  • Design robust systems for scaling model training across environments
  • Scale distributed training and inference on large GPU clusters
  • Enhance tooling and observability for faster ML production transitions

Benefits

  • Unlimited PTO for optimum work-life balance
  • Daily free lunch with teammates at the office
  • Reimbursed transportation expenses
  • Comprehensive health insurance package
  • Health and wellness budget of $150 per month
  • Flexible parental leave arrangements
Full Job Description
The Opportunity

As an ML Infra Engineer, you'll play a key role in building the inference and training frameworks that make it possible to deliver results at scale. You'll collaborate closely with our ML and Platform teams to scale training across nodes, develop faster and more efficient serving, and create observability across the stack. This is a high-impact role where you'll help define what high performance ML training and inference look like at Reducto.

What You'll Do
  • 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.
You'll Thrive Here If You:
  • Hold yourself to a high bar for quality and precision.
  • Enjoy solving complex problems and building from first principles.
  • 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.
Bonus points if you:
  • Have experience at an early-stage or high-growth startup.
  • Have developed in open source training/inference stacks in a meaningful way.
  • Are excited to set up distributed inference across 100s-1000s of GPUs.
  • Care deeply about combining technical excellence with business impact.

This is an in person role at our office in SF. We're an early stage company which means that the role requires working hard and moving quickly. Please only apply if that excites you.

More about Reducto

Nearly 80% of enterprise data is in unstructured formats like PDFs

PDFs are the status quo for enterprise knowledge in nearly every industry. Insurance claims, financial statements, invoices, and health records are all stored in a structure that's simply impractical for use in digital workflows. This isn't an inconvenience-it's a critical bottleneck that leads to dozens of wasted hours every week.

Traditional approaches fail at reliably extracting information in complex PDFs

OCR and even more sophisticated ML approaches work for simple text documents but are unreliable for anything more complex. Text from different columns are jumbled together, figures are ignored, and tables are a nightmare to get right. Overcoming this usually requires a large engineering effort dedicated to building specialized pipelines for every document type you work with.

Reducto breaks document layouts into subsections and then contextually parses each depending on the type of content. This is made possible by a combination of vision models, LLMs, and a suite of heuristics we built over time. Put simply, we can help you:
  • Accurately extract text and tables even with nonstandard layouts
  • Automatically convert graphs to tabular data and summarize images in documents
  • Extract important fields from complex forms with simple, natural language instructions
  • Build powerful retrieval pipelines using Reducto's document metadata
  • Intelligently chunk information using the document's layout data


Benefits at Reducto

At Reducto, we're invested in the well-being and growth of our team. Here's what we currently offer:
  • Unlimited PTO: We believe great work requires recharging.
  • Lunch: Receive a free lunch to eat with your teammates daily at the office
  • Reimbursed Transportation: Provide us with your receipts and we'll take care of the costs
  • Insurance: Generous health insurance covering medical, dental, and vision.
  • Health and Wellness Budget: We provide up to $150/mo reimbursement for health and wellness spending, such as gym memberships, fitness classes, or similar.
  • Parental Leave: Work with us to build a leave schedule that works for you and your family

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