Machine Learning Eval Engineer

Hold yourself to a high bar for quality and precision.

Enjoy solving complex problems and building from first principles.

Have strong Python skills and can independently build clean, reliable technical solutions. Bonus points for product and frontend experience!

Are comfortable working with data infrastructure such as AWS S3 and OLAP or analytics systems like Tinybird.

Love getting your hands dirty with unstructured data and chasing down difficult failure cases.

Operate well in fast-changing, high-growth environments.

Collaborate effectively across technical and non-technical teams.

Take full ownership from strategy through execution.

Have experience at an early-stage or high-growth startup.

Have some background in product thinking and can build simple, polished user-facing interfaces.

Are comfortable working directly with customers to understand their workflows and data needs.

Have experience in AI/ML, data infrastructure, enterprise software, or document understanding systems.

Care deeply about combining technical excellence with business impact.

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