Build and maintain ML evaluation benchmarks and metrics to identify model weaknesses on unstructured enterprise data like PDFs and spreadsheets. Collaborate with ML teams to drive improvements using Python tools and data infrastructure.
150k – 300k/yr
On-siteML Engineering
About the role
What You’ll Do
Design, build, and maintain evaluation benchmarks that reveal where our models perform well and where they fail.
Develop metrics, heuristics, and workflows to automatically identify new failure modes across large and messy real-world datasets.
Partner closely with other ML engineers to turn evaluation insights into model improvements and better training priorities.
Work hands-on with unstructured enterprise data, including PDFs, spreadsheets, and other difficult document formats, to uncover edge cases and hard examples.
Build lightweight internal and user-facing tools, including simple interfaces in Python frameworks like Flask, to help teams inspect results, analyze model behavior, and communicate evaluation outcomes.
Collaborate with customers and internal teams to understand real-world data needs and create bespoke benchmarks that highlight Reducto’s strengths.
You’ll Thrive Here If You
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.
Bonus points if you
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.
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