AI role · Open offer

AI Agent Engineer — tool use & orchestration

Anthropic

Full-time
Senior
Applied agents
Hybrid
548 Market Street, San Francisco, CA 94104, United States · Hybrid
San Francisco, United States
USD 185k–250k + 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.

4 skills2 tools

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: USD 185k–250k + equity
Experience level: Senior
Team: Applied agents
Benefits & perks

About this role

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

Anthropic is hiring a Senior AI Agent Engineer — tool use & orchestration on the Applied agents 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 builds Claude and safety-focused AI systems from San Francisco.

AI Agent Engineer (hybrid SF): design tool-use loops, memory, and orchestration for production agents with measurable reliability.

Missions

Ship agent workflows with tool schemas, retries, and human-in-the-loop gates.
Own eval harnesses for task success, cost, and safety regressions.

Stack & environment

Python, LangChain / custom orchestration, Docker, Kubernetes.

What we look for

Production agents or copilots; strong eval instincts; US work auth.

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

Tools & stack

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

KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
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: Share an agent with tool-use loops, evals, and production guardrails.
Several of these show up in recent shipped work—not only on your CV: Python, LangChain, OpenAI API, NLP, and Kubernetes.
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.

Apply

Candidate accounts only: add your headline and bio on , 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

Share an agent with tool-use loops, evals, and production guardrails.

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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