Backend/AI Engineer

🇺🇸 San Francisco, California
$2K - $3K Annual
Posted 5 months ago
Expires June 9, 2026
Full TimeOn-siteEngineeringProduct

ABOUT REDUCTO

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, growing revenue by 8x YOY, and now work with hundreds of companies ranging from leading AI teams (Harvey, Vanta, Scale), through to enterprise (FAANG, top 3 trading firm).

We've raised over 100M from world class investors like A16z, Benchmark, and First Round Capital, and are looking for a Backend/AI Engineer.

THE OPPORTUNITY

As a Backend/AI Engineer, you'll work on our core API that powers document parsing for hundreds of companies. You'll integrate cutting-edge LLMs, optimize document processing pipelines, and build the infrastructure that makes state-of-the-art document understanding accessible at scale.

WE WOULD LOVE TO MEET YOU IF YOU:

- Philosophy: You are your own worst critic. You have a high bar for quality and don't rest until the job is done right—no settling for 90%. We want someone who ships fast, with high agency, and who doesn't just voice problems but actively jumps in to fix them.

- Experience: You have 2+ years of experience building real world applications and integrating LLMs into production systems

- Language: You're exceptional at Python

- Tools: Build your own tools as needed—like a quick Streamlit app to test hypotheses or create a dataset.

- Approach: A quantitative approach to building products. Ability to debug, experiment, and iterate fast. You should be comfortable getting hands-on with the full development lifecycle, from ideation to shipping to users.

THE CORE WORK WILL INCLUDE:

- Integrating and optimizing LLM calls for structured extraction, form filling, and document unde...

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