Role Overview

We’re looking for a senior operator to own the quality bar behind Harvey’s human evaluations. As we scale globally, the volume of eval work is growing 10x, but volume only matters if the output is trusted. This role makes Human Data’s signal decision-grade: rigorous, calibrated, and reproducible enough that Product, Engineering, and AI Research act on it to ship.

As a member of our Evaluation Operations team, you’ll work alongside our Evaluation Operations Manager (who runs throughput and coordination) and partner closely with Applied Legal Researchers, Product, Engineering, and AI Research. You'll set the standard for what "good" looks like across eval data and methodology, and own the data analyses to make conclusions that teams will rely on, building the stakeholder trust that lets EPD act on the signal.

What You'll Do

• Own the quality bar for Harvey’s human evaluations: define what “good” looks like for eval methodology and data analysis, and produce decision-grade outputs for EPD

• Author and maintain the evaluation guidelines, instructions, and databases that contract attorneys work from

• Standardize and streamline rubric and evaluation design into repeatable templates and one documented methodology, partnering with Applied Legal Research (ALR), who supplies feature-specific legal depth

• Own contract-attorney quality: onboarding, calibration training, inter-rater reliability, and the feedback loop (including benchmarks and gold references) that keeps judgment consistent across attorneys and over time

• Conducting quantitative and qualitative statistical analyses, diagnosing error states, investigating root causes, and turning raw eval results into a structured, prioritized signal Product and ALR can act on

• Run QA on vendor and contract-attorney deliverables against a defined bar before results inform a launch decision

• Ensure the quality bar holds across jurisdictions and non-English geographies as coverage expands

• Establish one standard, documented way to analyze eval results, and build lightweight operational dashboards to track rater capability and eval-program health

• Support ALR in a review step that certifies an evaluation is sound before it scales to contract attorneys

• Partner with ALR and Analytics to determine where human eval aligns with online signal and where it can provide expanded insights

What You Have

• 6+ years in product operations, research operations, evaluation/QA operations, or quality program management

• A track record of owning quality inputs (guidelines, instructions, benchmarks, QA procedures) for complex, expert-driven or human-in-the-loop work

• Experience onboarding, training, and calibrating a distributed pool of expert raters, annotators, or reviewers, and running the feedback loop that improves their quality over time

• Enough grounding in measurement concepts (calibration, inter-rater reliability, sampling, rubric design) to independently set up and own the quality of our evaluation loop yourself

• Experience with running quantitative and qualitative data analyses, interpreting and running statistical tests on evaluation data (natively or with AI tool support), and communicating conclusions to various stakeholders

• A record of scaling and streamlining evaluation quality processes under shipping pressure, with a bias toward documentation and reproducibility over one-off analysis

• Ability to work deeply with domain experts (e.g., ALR / lawyers) and translate nuanced judgment into repeatable, documented standards

• Strong cross-functional coordination across Product, Engineering, Research, ALR, and data providers/vendors

• Clear communicator who can build credibility and trust with stakeholders

• Bias to action and high ownership, from writing the guideline to auditing a vendor batch line by line

Bonus Points

• Experience in legal tech or working with domain experts in regulated industries

• Experience owning quality across multiple markets, languages, or jurisdictions

• Built calibration, inter-rater reliability, or capability-tracking systems for annotation or evaluation pipelines

• Experience transitioning evaluation work in-house or otherwise improving evaluation ROI

• Familiarity with LLM-as-judge / automated evaluation used alongside human eval

• Early employee at a hyper-growth startup, or experience at a world-class product or platform operations org

Compensation

$155,400 - $233,200 USD

Depending on your location, an Applicant Privacy Notice may apply to you. You can find all of our Applicant Privacy Notices here.

#LI-CD1