Machine Learning Eval Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Learning budget

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Machine Learning Eval Engineer San Francisco Office About Reducto Reducto is the agentic document platform for leading AI teams who demand enterprise performance at scale. We provide a comprehensive toolkit for working with documents the way a human would, combining custom in-house and leading frontier models to power efficient and accurate document workflows. We've grown rapidly, increasing revenue 8x year over year and partnering with hundreds of companies, from leading AI teams like Harvey, Vanta, and Scale, to enterprise customers across FAANG and top trading firms. Reducto has raised over $100M from world-class investors including a16z, Benchmark, and First Round Capital. The Opportunity As an ML Eval Engineer, you'll play a key role in building the evaluation systems and benchmarks that make Reducto's models better over time. You'll collaborate closely with our ML, platform, and GTM teams to identify model weaknesses, design strong benchmarks, and create metrics and tooling that surface new failure modes as we scale. This is a high-impact role where you'll help define how model quality is measured at Reducto and shape the systems we use to improve it. 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

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