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LLM Engineer — Legal Copilots | Harvey | SF · Ganloss

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LLM Engineer — legal copilots

AI role · Open offer

LLM Engineer — legal copilots

Harvey

Full-time
Senior
Legal AI
Hybrid
575 Market Street, San Francisco, CA 94105, United States · Hybrid
San Francisco, United States
$190k–$260k + equity

Employer

Harvey

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Posted on Ganloss

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: 575 Market Street, San Francisco, CA 94105, United States · Hybrid
Compensation: $190k–$260k + equity
Experience level: Senior
Team: Legal AI
Benefits & perks: Top-tier comp, equity.
How to apply: Legal or citation-heavy RAG example.
Company site: www.harvey.ai
Listed: Jul 13, 2026
Updated: Aug 3, 2026

About this role

What the team is looking for and how this position fits.

Harvey is hiring a Senior LLM Engineer — legal copilots on the Legal AI team (Hybrid · San Francisco, United States). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Harvey builds LLM copilots for law firms with strict confidentiality and citation requirements.

LLM Engineer (San Francisco): legal RAG, prompt systems, and human-in-the-loop review tooling.

What you will do

Deliver production-grade legal-domain LLM products with measurable quality, latency, and cost targets.
Build offline evals and online monitoring before scaling to full traffic.
Collaborate with product, design, legal, or domain experts on safe rollout.
Write clear RFCs, runbooks, and postmortems for cross-team learning.

What we look for

Multiple shipped ML/LLM features—not coursework-only portfolios.
Strong Python, SQL, and experimentation discipline.
Clear communication for remote or hybrid stakeholders.
Work authorization for the listed country.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
OpenAI API
Calling frontier APIs with retries, cost controls, prompt versioning, and safety filters.

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

Vector DBsTool
Embedding storage, hybrid search, and freshness strategies for RAG and semantic retrieval.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Legal or citation-heavy RAG example.
Several of these show up in recent shipped work—not only on your CV: Python, LangChain, OpenAI API, and Vector DBs.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · San Francisco, United States)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: $190k–$260k + equity, benefits in At a glance) and your expectations align.

Apply

Candidate accounts only: add your headline and bio on My account, and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

Legal or citation-heavy RAG example.

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Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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LLM Engineer — legal copilots

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