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AI role · Open offer

Research Engineer — LLM alignment

Anthropic

Full-time
Senior
Alignment
Hybrid
548 Market Street, San Francisco, CA 94104, United States · Hybrid
San Francisco, United States
$200k–$280k + equity

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

3 skills1 tool

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At a glance

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

Employment: Full-time
Workplace: Hybrid
Location: 548 Market Street, San Francisco, CA 94104, United States · Hybrid
Compensation: $200k–$280k + equity
Experience level: Senior
Team: Alignment
Benefits & perks: Top-tier medical, equity, research time.
How to apply: Summarize an RLHF or eval project you contributed to.
Company site: www.anthropic.com
Listed: Jun 6, 2026
Updated: Aug 3, 2026

About this role

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

Anthropic is hiring a Senior Research Engineer — LLM alignment on the Alignment 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.

Anthropic researches AI safety and builds Claude—frontier language models deployed to enterprises with strong harmlessness and honesty goals.

Research Engineer (San Francisco): RLHF pipelines, evals, red-teaming automation, and scalable training infrastructure partners.

What you will do

Ship production alignment and LLM training with clear evaluation, monitoring, and rollback paths—not demo-only notebooks.
Partner with product, design, and domain experts on requirements, trade-offs, and safe rollout.
Improve data quality, labeling, and offline metrics that correlate with business KPIs.
Document architectures and runbooks for on-call and customer-facing teams.
Mentor engineers on testing, observability, and responsible AI practices.

What we look for

Strong Python and modern ML/LLM stack experience with multiple shipped features.
Comfort with SQL, experimentation, and communicating results to non-technical stakeholders.
Pragmatic ownership from prototype to production—including cost and latency budgets.
Eligible to work in the listed country; professional English (French a plus for Paris roles).

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.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.

Tools & stack

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

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

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: Summarize an RLHF or eval project you contributed to.
Several of these show up in recent shipped work—not only on your CV: Python, LangChain, PyTorch, and Docker.
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: $200k–$280k + 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

Summarize an RLHF or eval project you contributed to.

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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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