We are looking for an Infrastructure Engineer with 2–5+ years of experience to own end-to-end deployment of Reducto into enterprise customer environments – from VPC to bare metal on-prem. You'll be the engineer who makes complex deployments work, partnering directly with customer IT, security, and platform teams. This role requires strong technical chops in Kubernetes, networking, and cloud infrastructure, combined with excellent communication skills – you'll be in live channels and on calls with customer engineers and senior leaders. We have a team lead already in place and are looking for both a mid-career engineer and a more junior hire to scale the team.
What you will be doing
Leading end-to-end deployment of Reducto into customer environments – planning, configuration, testing, and rollout across VPC and on-prem setups
Working directly with enterprise customer teams (e.g., Harvey) – sometimes visiting their offices, communicating in shared channels with their engineers and senior leaders
Debugging and resolving customer-specific infrastructure issues like K8s misconfigurations, cloud IAM edge cases, and networking problems
Building monitoring, telemetry, and automation – turning one-off customer work into repeatable deployment patterns that scale across future deployments
Understanding the hardware powering Reducto's ML models to successfully deploy them onto customer-controlled infrastructure
The vast majority of enterprise data is in files like PDFs and spreadsheets. That includes everything from financial statements to medical records. Reducto helps AI teams turn those really complex documents into LLM-ready inputs with exceptional accuracy.
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.
Hundreds of companies have signed up to use Reducto since our launch, and we’re now processing tens of millions of pages every month for teams ranging from startups to Fortune 10 enterprises. We are built for enterprise workloads with flexible deployment options from the cloud to fully air-gapped environments, SOC II and HIPAA compliance, and zero data retention.
Machine Learning Eval Engineer
Lead Software Engineer, Platform
ML Infrastructure Engineer
Forward Deployed Engineer
Machine Learning Engineer
Salary
$200,000 - $300,000
Location
San Francisco, United States
Experience
2+ years
Total raised
$108.0M
Last stage
Series B
Investors
No applications, no recruiter spam. Just the intro.
A few questions to make sure this role is the right shape for you. Two minutes.
I write the intro, send it to the founder, and handle the back-and-forth.
If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.