The Safeguards ML Infra team designs, builds, and operates the production infrastructure that powers Claude's safety systems. You will lead operational work to configure safeguards for new model launches, manage off-cycle deployments of safety classifiers, automate release runbooks, maintain a safeguards registry, and participate in on-call rotations.
Staff+ Site Reliability Engineer, Safeguards ML Infra en Anthropic
Remoto - Remote (Travel required), San Francisco, CA, Seattle, WA, New York City, NY
Más vacantes en AnthropicSalary
USD 320,000 - 485,000
Requirements
Skills
- Have owned production change management at scale — deploy pipelines, config management systems, canary analysis — and have strong opinions about what "verified" means.
- Have run high-stakes releases: served as a launch captain, incident commander, or release owner for systems where a bad deploy has real consequences, and are energized rather than drained by being in the critical path.
- Have meaningful on-call experience for production systems, including incident response and postmortem-driven improvements — and a track record of turning (and fixing!) postmortem action items into process and tooling changes.
- Have a desire to close the gap where nobody has yet raised their hand, even if it requires manually hand-holding processes until automation and tooling can be built.
- Have hands-on experience deploying and operating on cloud platforms (AWS, GCP) at scale.
- Are proficient in Python; experience with Rust is a plus but not required.
- 8+ years of industry software engineering or site reliability engineering experience.
- A demonstrated history of reducing operational toil through automation, including transitioning teams from manual deployment processes to self-serve pipelines.
- Experience running launch or production-readiness review processes across multiple teams.
- Familiarity with LLM inference systems and the operational characteristics of transformer-based models.
Responsibilities
- Launch captain model releases: stand up, configure, and verify safeguards for every new model, and serve as the safeguards point of contact in the launch room during release windows.
- Own the off-cycle deployment of new safety classifiers as they ship from research — canarying rollouts, running post-deploy validations, and investigating discrepancies when something looks wrong.
- Verify that the right safeguards are provably live on the right models across every deployment platform (1P, AWS Bedrock, GCP Vertex, etc.), and detect and eliminate configuration drift between them.
- Automate yourself out of last quarter's work: turn launch runbooks into tooling, hand-built checks into continuous validation, and one-off deploys into a repeatable pipeline.
- Build and maintain a safeguards registry with full provenance — what is running in production, on which model, on which platform, and when and by whom it was deployed.
- Participate in on-call and operational-duty rotations covering service incidents, model provisioning, and time-sensitive research and safety launches.
Technologies
AWSGCPPythonRustLLM inference systemsTransformer-based models
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