---
title: 'Machine Learning Infrastructure Tech Lead at Reducto'
canonical: 'https://feeny.ai/job/machine-learning-infrastructure-tech-lead-reducto-san-francisco-gya96jx3b2wt'
type: 'job'
last_seen: '2026-09-07'
---

# Machine Learning Infrastructure Tech Lead at Reducto

- **Company:** Reducto
- **Location:** San Francisco, CA
- **Compensation:** $200k–$325k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-07-16
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/reducto/36de424a-cc97-4972-83c8-0a46b9954f65

## Job description

## 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.
- Speed: We move fast, ship often, and iterate in days, not months.
- Learning: Work alongside world-class engineers, operators, and founders who care deeply about product, precision, and velocity.

## Benefits

- Unlimited PTO, because great work requires recharging.
- Daily Lunch, enjoy free lunch with teammates in the office.
- Commuter Reimbursement, we’ll cover your transportation costs.
- Comprehensive Insurance, medical, dental, and vision.
- Health and Wellness Budget, up to $150 per month for wellness spending such as gym memberships or fitness classes.
- Parental Leave, flexible scheduling that works for you and your family.Working at Reducto

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

## Equal Opportunity

Reducto is an Equal Opportunity Employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration without regard to sex, race, color, age, national origin, religion, disability, sexual orientation, gender identity, veteran status, or any other protected category.

## About Reducto

## Company Overview
- **One-liner**: Reducto is the agentic document platform for leading AI teams, providing a comprehensive toolkit for document parsing, extraction, and automation at enterprise scale.
- **Entity Type**: Private (Series B)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2023
- **Founders**: Adit Abraham (CEO) and Raunak Chowdhuri (CTO)

## Core Business
- **Primary industry**: Document AI / Enterprise Software / Artificial Intelligence
- **Target customers**: AI teams and enterprises across legal, finance, healthcare, and insurance sectors — ranging from startups to Fortune 10 companies.
- **Mission or purpose**: To unlock messy, real-world document data so that leading AI teams can build smarter systems, faster decisions, and better products.

## Products & Services
- **Parse**: Reads documents like a human would, capturing layout, structure, and meaning with high accuracy. Uses Agentic OCR that reviews and corrects outputs in real-time for near-perfect results.
- **Extract**: Extracts structured data directly from documents with schema-level precision — invoice fields, onboarding forms, financial disclosures, and more.
- **Edit**: Dynamically fills in detected blanks, tables, and checkboxes with supplied data across scanned PDFs, digital forms, and complex multi-page documents — no bounding boxes or templates required.
- **Split**: Automatically separates multi-document files or long forms into individually useful units using intelligent, layout-aware heuristics.
- **Deep Extract**: The most accurate structured document extraction agent yet, leveraging vision-language models for context-aware extraction.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Total Funding**: $108.4M across four rounds
  - Pre-Seed (Apr 2024): $500K led by Y Combinator
  - Seed (Oct 2024): $8.4M led by First Round Capital
  - Series A (Apr 2025): $24.5M led by Benchmark
  - Series B (Nov 2025): $75M led by Andreessen Horowitz
- **Notable Investors**: Benchmark, First Round Capital, Andreessen Horowitz, Box Group, Y Combinator, and founders of Dropbox and Airtable.
- **Growth Signals**: Headcount grew 313.3% YoY to 43 employees; trusted by leading AI teams including Harvey, Scale AI, and Vanta; serves Fortune 10 enterprises; operates in 6 countries.

## Competitive Advantages
- **Agentic OCR**: Combines custom in-house vision models with leading frontier VLMs that review and correct outputs in real-time, achieving near-perfect accuracy even on edge cases.
- **Enterprise-grade security**: SOC 2 and HIPAA compliant, with flexible deployment options from cloud to fully air-gapped environments and zero data retention.
- **Battle-tested infrastructure**: 99.9%+ uptime, built for enterprise workloads at scale.
- **Deep technical moat**: Founders are MIT CS graduates with published computer vision research and experience at Google and MIT Media Lab.

## Strategic Focus
- Scaling the platform to serve more enterprise customers across legal, finance, healthcare, and insurance verticals.
- Continuing to advance proprietary vision-language models for document understanding.
- Expanding the team, particularly in engineering, machine learning, and go-to-market roles.

## Why Work Here
- **Culture**: "We stay small on purpose, so ideas move fast, ownership runs deep, and your work directly shapes the future of the company." The team is described as builders — curious, humble, and relentless about solving real problems.
- **Team**: 43 employees with strong talent from MIT, Stanford, and companies like Scale AI, Vanta, Meta, and Hudson River Trading. Technical roles make up 27% of the team.
- **Location**: San Francisco office with daily lunch, snacks, and commute stipends.
- **Benefits**: High-quality medical, dental, and vision coverage; 401k with 4% company match; health and wellness stipends (gym, fitness classes); parental leave; flexible PTO.
- **Open roles**: 35 active job postings across engineering, ML, product, customer success, and sales — all full-time in San Francisco.

## Sources
1. [reducto.ai](https://reducto.ai/)
2. [reducto.ai/careers](https://reducto.ai/careers)
3. [ycombinator.com/companies/reducto](https://www.ycombinator.com/companies/reducto)
4. [linkedin.com/company/reducto-ai](https://www.linkedin.com/company/reducto-ai)
5. [cbinsights.com/company/reducto-ai](https://www.cbinsights.com/company/reducto-ai)
6. [fortune.com](https://fortune.com/2025/04/01/reducto-ai-document-parsing-startup-raises-24-5-million-series-a-led-by-benchmark/)

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