Staff+ Site Reliability Engineer, Safeguards ML Infra

FDE Times; San Francisco (CA); Hybrid; USD 320,000 - 485,000; Full time

Job summary

Anthropic is seeking an engineer to own and automate Safeguards deployment for model launches, ensuring robust safety systems across platforms (1P, AWS Bedrock, GCP Vertex). You will lead canary rollouts, configure safeguards, and drive continuous validation to reduce manual work.

You will bring hands-on experience with production releases, incident response, and drive efficiency by turning runbooks into tooling, while collaborating across research and platform teams in a hybrid,

Qualifications

• Experience in production change management at scale and deployment pipelines.
• Experience conducting launch readiness or incident response in production environments.
• Ability to automate deployment and validation tasks into repeatable pipelines.

Responsibilities

• Launch captain model releases: stand up, configure, and verify safeguards for every new model.
• Own off-cycle deployment of new safety classifiers with canarying and post-deploy validation.
• Verify safeguards are live across all deployment platforms and detect drift.
• Automate hand-built checks into continuous validation and pipelines.
• Turn runbooks into tooling and one-off deploys into repeatable processes.
• Participate in on-call rotations covering incidents and time-sensitive launches.

Skills

Launch captain

Incident commander

Release owner

Automation

Production change management

Cloud platforms (AWS, GCP)

Python

System reliability

Education

Bachelor’s degree or equivalent

Tools

AWS

GCP

Rust

Python

Job description

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

The Safeguards ML Infra team designs, builds, and operates the production infrastructure that powers Claude's safety systems. We own the critical backend services that ensure safety on the token generation path, and we own the operational work of getting those systems safely into production: standing up safeguards for every new model launch, and deploying new safety classifiers as they ship. Every frontier model release runs through this team – we configure, verify, and roll out safeguards across every platform Claude runs on (1P, AWS Bedrock, GCP Vertex, etc.), and we lead incident response when issues arise.

This role sits at the center of that operational work. You'll ensure safeguards are properly configured and deployed for model launches and own the off-cycle deployment of new safety classifiers — canarying changes, verifying that the right safeguards are provably live on the right models, and holding rollback authority when something looks wrong. Every launch should also shrink the checklist, and the manual verifications should evolve into a system that runs itself. You'll turn launch runbooks into tooling, hand-built checks into continuous validation, and one-off deploys into a repeatable pipeline.

We're looking for engineers with deep experience in production change management at scale — people who have owned deploy pipelines, config management systems, rollout safety, or launch readiness for systems under real production pressure. Familiarity with ML research or transformer architectures is not required — you will learn that on the job. What we prioritize is production judgment: a track record of shipping changes to critical systems safely, and of automating yourself out of the work you did last quarter.

What you'll do

• 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.
• Plan to use Claude aggressively to do this! And be a trailblazer that paves the path for safe agentic operations of safety-critical systems.
• 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.

You may be a good fit if you

• 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.

Strong candidates may also have

• 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.

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary

$320,000—$485,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.