Founding Customer Success Manager

About Us

Reducto helps AI teams ingest real world enterprise data with state of the art accuracy.

The vast majority of enterprise data — from financial statements to health records — is locked in unstructured file formats like PDFs and spreadsheets. We train vision models to read those documents the way a human would, and make it possible to build products, train models, and automate processes at scale.

We’ve grown incredibly quickly, from zero to mid 7 figure ARR in 1 year, and now work with hundreds of companies ranging from leading AI teams (Harvey, Vanta, Scale), through to enterprise (FAANG, top 3 trading firm).

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

The Opportunity

As our founding Customer Success hire, you'll build our customer success function from the ground up while managing a portfolio of high-value customers to drive logo retention and NRR. You'll work directly with technical teams using Reducto to solve complex document processing challenges, ensuring they get maximum value from our platform. This is a unique opportunity to shape how we deliver customer success at a rapidly growing AI infrastructure company.

We would love to meet you if the following resonates:

• You're Strategic and Technical: You can dive deep with technical teams to understand their document processing challenges while maintaining a strategic view of their business objectives. You're comfortable discussing both technical implementation details and big-picture business outcomes.

• You're Process-Oriented: You know how to build scalable success processes and can establish the foundations for a world-class CS organization.

• You're Data-Driven: You're skilled at tracking and analyzing customer health metrics, usage data, and business outcomes. You use data to drive your decisions and to demonstrate clear ROI to customers.

• You're an Exceptional Communicator: You can effectively engage with both technical practitioners and executive stakeholders. You excel at written documentation, customer training, and leading strategic business reviews.

• You're Proactive and Independent: You don't wait for problems to surface – you actively monitor customer health and spot opportunities to drive additional value. You're comfortable working autonomously while building processes for scale.

The core work will include:

70% of Time

• Managing a portfolio of enterprise customers from implementation through expansion

• Conducting regular business reviews and maintaining strong relationships with key stakeholders

• Monitoring customer health metrics and proactively addressing potential issues

• Creating and executing customer success playbooks for different customer segments

30% of Time

• Building our customer success function from the ground up

• Establishing processes for onboarding, training, and ongoing customer engagement

• Creating scalable customer enablement content and documentation

• Partnering with Product and Engineering to represent the voice of the customer

You are likely a strong fit if:

• You’ve worked with Enterprise clients in consulting for 4+ years

• You’re familiar with ML/AI infrastructure or developer tools

• You have a technical background or strong interest in AI/ML

Bonus points if you:

• Have previously worked at a high-growth startup

• Bring established relationships in the ML/AI ecosystem

• Have demonstrable experience growing accounts and driving expansions

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.

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