Original listing text, shown exactly as published by the company.
What You'll Do
- Build and scale the systems that power model and product evaluations across Harvey
- Run intake, triage, and prioritization for the evaluation request queue, routing capacity to the highest-value coverage gaps
- Embed evaluation workflows and readiness checkpoints into the product development lifecycle
- Create the single source of truth for evaluation status, results, history, and launch readiness
- Turn Expert-designed evaluation methodologies into scalable, repeatable operational processes
- Manage human data providers and stand up our internal contract-attorney pipeline, ensuring evaluation quality meets legal standards
- Work with Engineering and Research to improve evaluation tooling, automation, and dashboards
- Drive evaluation readiness for major product and model launches across geographies and jurisdictions
- Document and operationalize evaluation governance as complexity increases
- Help define how Harvey ensures model accuracy, reliability, and trust at global scale
What You Have
- 4–7+ years in technical program management, product operations, research operations, or evaluation/benchmarking roles
- Experience working with ML/AI evaluations, benchmarking frameworks, or scientific workflows
- Comfort with statistical methodologies and SQL or Python, or similar tools to interpret evaluation data (either natively or with AI tool support)
- Strong business acumen with an ability to apply an ROI-focused mindset to scaling
- Ability to work deeply with legal experts and operationalize complex evaluation methodologies
- Strong cross-functional coordination skills across Product, Engineering, Research, and data providers/vendors
- High attention to detail and a bias toward clarity, rigor, and reproducibility
- Ability to navigate an evolving landscape and bring order to complex systems
- Strong communication skills and comfort translating technical nuance for diverse stakeholders
- Desire to do whatever it takes to make evaluation systems successful—from writing documentation to diagnosing pipeline issues
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