Machine Learning Infra Engineer

About the role

ABOUT REDUCTO

Reducto is the agentic document platform for leading AI teams who demand enterprise performance at scale. We provide a comprehensive toolkit for working with documents the way a human would, combining custom in-house and leading frontier models to power efficient and accurate document workflows.

We’ve grown rapidly, increasing revenue 8x year over year and partnering with hundreds of companies, from leading AI teams like Harvey, Vanta, and Scale, to enterprise customers across FAANG and top trading firms.

Reducto has raised over $100M from world-class investors including a16z, Benchmark, and First Round Capital.

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.

- Have 3+ years of experience.

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.