---
title: 'Research Engineer / Scientist, Data & Evals at Harvey'
canonical: 'https://feeny.ai/job/research-engineer-scientist-data-evals-harvey-new-york-58pmg71vb4vc'
type: 'job'
last_seen: '2026-11-09'
---

# Research Engineer / Scientist, Data & Evals at Harvey

- **Company:** [Harvey](https://feeny.ai/companies/harvey)
- **Location:** New York, NY
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-11-02
- **Last confirmed live:** 2026-11-09
- **Apply:** https://jobs.ashbyhq.com/harvey/46912896-5a97-4955-ae27-1b110b3487ec

## Job description

## Why Harvey

At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started.

## Role Overview

In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at [harvey.ai/research](http://harvey.ai/research).

## What You'll Do

- Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows.
- Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail.
- Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows.
- Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation.
- Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination.
- Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most.
- Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions.

## What You Have

- Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code.
- A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead.
- The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria.
- Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work.

We are hiring for this role across multiple levels.

## Compensation

$230,000 - $450,000 USD

Depending on your location, an Applicant Privacy Notice may apply to you. You can find all of our Applicant Privacy Notices [here](https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74).

## #LI-DZ1

Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

## About Harvey

## Company Overview
- **One-liner**: Harvey builds domain-specific AI software for legal and professional services, enabling law firms and in-house teams to automate research, contract analysis, due diligence, and complex workflows.
- **Entity Type**: Private (backed by leading venture capital firms)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2022
- **Founders**: Winston Weinberg (CEO), Gabriel Pereyra (President)

## Core Business
- **Primary industry**: AI software for legal and professional services (LegalTech)
- **Target customers**: B2B – large law firms (AmLaw 100), in-house legal teams at Fortune 500 enterprises, mid-sized firms, and professional service networks across 60+ countries.
- **Mission or purpose statement**: To enable legal and professional services teams to focus on high-value work by providing a secure, expert AI platform that handles research, drafting, and complex workflows.

## Products & Services
- **Harvey Assistant**: Ask questions, analyze documents, and draft faster with domain-specific AI (SaaS).
- **Harvey Vault**: Securely store, organize, and bulk-analyze legal documents (SaaS).
- **Harvey Knowledge**: Research complex legal, regulatory, and tax questions across domains (SaaS).
- **Harvey Agents**: Purpose-built agents that execute complex legal work end-to-end (SaaS).
- **Harvey Contract Intelligence**: Surface insights, strengthen negotiations, and accelerate reviews (SaaS).
- **Harvey Command Center**: Analytics, benchmarking, and agentic insights for leading AI transformation (SaaS).
- **Harvey Mobile**: Mobile access to stay productive from anywhere (SaaS).
- **Harvey Shared Spaces**: Collaborate with legal teams across organizations in secure shared workspaces (SaaS).
- **Harvey Ecosystem**: Integration with existing tools to ground answers in trusted sources (API/platform).

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed (company is private).
- **Key Metric**: Annual Recurring Revenue (ARR) – reported in press but exact figure not disclosed; strong growth implied by 1,500+ customers and rapid hiring.
- **Notable Investors/Partners**: Sequoia, Kleiner Perkins, GV (Google Ventures), OpenAI Startup Fund, Coatue, Andreessen Horowitz, EQT.
- **Growth Signals**: 
  - Named Time100 Most Influential Companies (2025).
  - LinkedIn Top 50 Startup of 2025.
  - CNBC Disruptor 50 List.
  - 1,500+ customers across 60+ countries, including AmLaw 100 firms.
  - Headcount: ~300 employees (as of mid-2025), with aggressive hiring (254 active job postings).
  - Office expansion: San Francisco, New York City, London.

## Competitive Advantages
- **Domain specialization**: AI models trained specifically for legal and professional services, not generic LLMs.
- **Enterprise security**: SOC 2 Type II, ISO 27001/27701/42001, GDPR, CCPA compliance – meeting law firm security requirements.
- **Deep investor backing**: World-class VC network provides strategic guidance and access.
- **Strong product‑market fit**: Rapid adoption by top-tier law firms and in-house teams, with high switching costs due to embedded workflows.
- **First‑mover in agentic legal AI**: Purpose‑built agents execute end‑to‑end tasks, differentiating from simple document Q&A tools.

## Strategic Focus
- **Customization, collaboration, and simplicity**: Helping teams embed expertise into Harvey, collaborate across firms, and reduce complexity via agentic workflows.
- **Expansion beyond legal**: Long‑term ambition to transform professional services broadly (consulting, accounting, etc.).
- **Product innovation**: Continued investment in agentic AI, mobile access, and ecosystem integrations.
- **Global growth**: Scaling sales and engineering teams in existing hubs and expanding into new markets.

## Why Work Here
- **Culture**: Fast‑paced, collaborative, ambitious. Values include **Decisiveness** (“take the square root of the weather” – avoid overanalysis), **Simplicity**, and **Job’s Not Finished** (continuous improvement).
- **Work environment**: Hybrid – 3+ days per week in office for roles based in San Francisco, New York City, or London. Some fully remote positions available (clearly marked).
- **Benefits**: Comprehensive health/dental/vision insurance, 401(k) match, paid parental leave (immediately eligible), annual professional development stipend, in‑office daily lunch, and more.
- **Growth opportunities**: Rapidly scaling (~300 employees, 254 open roles) provides internal mobility and ownership of impactful projects.
- **Engineering culture**: Strong blend of AI research (DeepMind background) and legal domain expertise. CTO Siva Gurumurthy recently joined. Emphasis on building secure, enterprise‑grade products.

## Sources
1. [harvey.ai](https://www.harvey.ai/)
2. [harvey.ai/company](https://www.harvey.ai/company)
3. [harvey.ai/blog/landing-a-job-at-harvey](https://www.harvey.ai/blog/landing-a-job-at-harvey)
4. [linkedin.com/company/harvey-ai](https://www.linkedin.com/company/harvey-ai)
5. [jobs.ashbyhq.com/harvey](https://jobs.ashbyhq.com/harvey)

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