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. This means they can build more reliable products while saving engineering time.
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.
As a member of our founding team, you'll play a key role in ensuring reliability, scalability, and performance of Reducto’s API and on prem deployments. You will:
Design, implement, and maintain core components of Reducto infrastructure with Terraform/Terragrunt/HELM (e.g. distributed/fair postgres queue, efficient autoscaling of workers) to support an ever growing amount of usage.
Improve on Reducto’s CI & staging systems to scale to quickly running thousands of documents for every PR. Improve deployments with CD platform like ArgoCD.
Build up observability and monitoring capabilities to track key metrics as we scale.
Work directly with customers during Reducto’s on-premise deployments. This includes work for the deployment set up and on the ground support for in person deployments.
Are an autonomous and resourceful engineer with 3-6 years of experience building and deploying high reliability systems via Kubernetes, Terraform, and Argo CD.
Have a high bar for quality and craftsmanship, and a willingness to fix issues whenever they happen.
Have fundamental programming ability in a backend language of choice (Python, Rust, Go, etc.).
Have excellent communication skills. You’re able to collaborate well with team members and work directly with our customers.
Prior experience as a founder or founding engineer for a rapidly scaling team.
Experience managing systems with high bursts of concurrent usage.
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.
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 and even more sophisticated ML approaches work for simple text documents but are unreliable for anything more complex. Text from different columns are jumbled together, figures are ignored, and tables are a nightmare to get right. Overcoming this usually requires a large engineering effort dedicated to building specialized pipelines for every document type you work with.
Reducto breaks document layouts into subsections and then contextually parses each depending on the type of content. This is made possible by a combination of vision models, LLMs, and a suite of heuristics we built over time. Put simply, we can help you:
Accurately extract text and tables even with nonstandard layouts
Automatically convert graphs to tabular data and summarize images in documents
Extract important fields from complex forms with simple, natural language instructions
Build powerful retrieval pipelines using Reducto’s document metadata
Intelligently chunk information using the document’s layout data
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