Machine Learning Infrastructure Tech Lead

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 our ML Infrastructure Tech Lead, you'll own the systems that make high-performance model training and inference possible at Reducto.

This is a deeply hands-on role: roughly 80% of your time will be spent building, debugging, and optimizing our infrastructure. The remaining 20% will focus on setting technical direction - identifying bottlenecks, planning our infrastructure roadmap, and helping the ML and Platform teams make strong architectural decisions.

You'll work across the stack, from model-serving kernels and GPU utilization to distributed systems and Kubernetes. We're looking for someone with the experience and judgment to lead ambiguous, high-impact infrastructure projects while remaining close to the code.

This is a fully in-person role at our San Francisco office.

WHAT YOU'LL DO

- Own the technical direction and roadmap for Reducto's ML infrastructure.

- Build and maintain our training and inference stack, balancing fast experimentation with high-performance production serving.

- Optimize model serving at every layer, including kernels, runtimes, batching, scheduling, and distributed inference.

- Design systems for reliable multi-node, multi-GPU training and inference.

- Improve GPU utilization, latency, throughput, reliability, observability, and cost efficiency.

- Develop benchmarks that identify bottlenecks and guide infrastructure investments.

- Evaluate state-of-the-art advances in training and inference and apply the ones that matter.

- Build the tooling and abstractions that help ML engineers move quickly from experiments to production.

- Partner with ML and Platform engineers on architecture, capacity planning, and technical prioritization.

- Raise the engineering bar through design reviews, mentorship, and hands-on technical leadership.

YOU'LL THRIVE HERE IF YOU

- Have 5+ years of experience building production infrastructure, including significant ML systems experience.

- Have led complex technical projects from an ambiguous problem through production deployment.

- Are equally comfortable setting direction and personally implementing the hardest parts.

- Have strong Python and systems-engineering skills.

- Understand the performance characteristics of modern GPU training or inference workloads.

- Are comfortable with Kubernetes and distributed training or serving frameworks.

- Can reason across low-level model performance and higher-level platform architecture.

- Hold yourself to a high bar for quality, precision, and operational reliability.

- Operate well in a fast-changing, high-growth environment.

- Take full ownership from strategy through execution.

BONUS POINTS IF YOU

- Have optimized or implemented CUDA, Triton, or custom model-serving kernels.

- Have contributed meaningfully to frameworks such as vLLM, SGLang, PyTorch, TensorRT-LLM, Ray, or related open-source systems.

- Have operated distributed inference or training across hundreds or thousands of GPUs.

- Have built observability, scheduling, or capacity-management systems for GPU workloads.

- Have experience at an early-stage or high-growth startup.

- Care deeply about connecting technical excellence to measurable business impact.

WHY REDUCTO

- Impact: Your work directly shapes how the world’s best AI companies access and use enterprise data …