Staff+ Site Reliability Engineer, Safeguards ML Infra
Anthropic · Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle · Remote
Anthropic
Anthropic is an AI safety and research company founded in 2021 by former OpenAI executives, including siblings Dario and Daniela Amodei, who left to pursue a more safety-focused approach to building powerful AI systems. Its flagship product, the Claude family of large language models, competes directly with OpenAI's GPT series and Google's Gemini, and has become widely used in enterprise and developer settings, particularly for coding and reasoning tasks. The company positions itself distinctly within the AI industry by emphasizing interpretability and alignment research alongside commercial deployment, framing itself as a lab racing to build capable frontier models while trying to ensure they remain steerable and beneficial as capabilities scale.
Gig 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: $405,000 — $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. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic. com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic. com/careers directly for confirmed position openings. 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.
Requirements to meet
Disclaimer
Suggestions only — review each course yourself to judge whether it meets the role's requirements. Completing a course doesn't guarantee proficiency or that you'll qualify; hiring standards vary by employer.
Skills required
Machine Learning (required)
GapCourses that may help you meet this requirement:
Introduction to DevOps principles for machine learning
Microsoft Learn · beginner · Free · 33m · ★ 4.7
This course directly covers Machine Learning, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 1 hours.
Continuous deployment for machine learning
Microsoft Learn · beginner · Free · 22m · ★ 4.7
This course directly covers Machine Learning, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 0 hours.
Intro to Machine Learning
Kaggle Learn · beginner · Free
This course directly covers Machine Learning, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level.
Agentic Workflows (required)
GapCourses that may help you meet this requirement:
Understand features of Copilot Studio agents
Microsoft Learn · beginner · Free · 54m
This course covers Agentic Workflows, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 1 hours.
Hugging Face Agents Course
Hugging Face Learn · intermediate · Free
This course covers Agentic Workflows, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at an intermediate level.
Design ALM process for AI-powered business solutions
Microsoft Learn · intermediate · Free · 46m
This course covers Agentic Workflows, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at an intermediate level and can be completed in approximately 1 hours.
Python (required)
GapCourses that may help you meet this requirement:
PySpark & Python: Hands-On Guide to Data Processing
Coursera · beginner · $49/mo · 5h · ★ 4.4
This course directly covers Python, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 5 hours.
Data Analysis with Python
freeCodeCamp · beginner · Free
This course directly covers Python, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level.
Pandas
Kaggle Learn · beginner · Free
This beginner-level Pandas course helps build foundational Python skills relevant to the Staff+ Site Reliability Engineer, Safeguards ML Infra role's skill gap in Python.
AWS (required)
GapCourses that may help you meet this requirement:
AWS Fundamentals Specialization
Coursera · beginner · $49/mo · 40h · ★ 4.8
This course covers AWS, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 40 hours.
AWS Cloud Technical Essentials
Coursera · beginner · $49/mo · 20h · ★ 4.8
This course covers AWS, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 20 hours.
AWS Cloud Solutions Architect Professional Certificate
Coursera · intermediate · $49/mo · 62h · ★ 4.8
This course covers AWS, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at an intermediate level and can be completed in approximately 62 hours.
GCP (required)
GapCourses that may help you meet this requirement:
Machine Learning Operations (MLOps): Getting Started
Coursera · intermediate · $49/mo · 4h · ★ 4.0
This course covers GCP, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at an intermediate level and can be completed in approximately 4 hours.
Machine Learning Operations (MLOps) on Google Cloud Specialization
Coursera · intermediate · $49/mo · 16h · ★ 4.0
This course covers GCP, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at an intermediate level and can be completed in approximately 16 hours.
Natural Language Processing on Google Cloud
Coursera · advanced · $49/mo · 7h · ★ 4.4
This course covers GCP, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at an advanced level and can be completed in approximately 7 hours.
Change Management (required)
GapCourses that may help you meet this requirement:
Program Strategy, Governance and AI-Enabled Decision Making Specialization
Coursera · beginner · $49/mo · 34h · ★ 4.7
This course directly covers Change Management, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 34 hours.
Stakeholder Management
Coursera · beginner · $49 · 5h · ★ 4.6
This course directly covers Change Management, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 5 hours.
Get started with Engineering Change Management for Dynamics 365 Supply Chain Management
Microsoft Learn · intermediate · Free · 2h · ★ 4.8
This course directly covers Change Management, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at an intermediate level and can be completed in approximately 2 hours.
Large Language Models (required)
GapCourses that may help you meet this requirement:
Leverage AI tools and resources for your business
Microsoft Learn · beginner · Free · 42m · ★ 4.8
This course covers Large Language Models, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 1 hours.
Explore data roles and services
Microsoft Learn · beginner · Free · 22m · ★ 4.8
This course covers Large Language Models, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 0 hours.
Use AI for everyday tasks
Microsoft Learn · beginner · Free · 43m
This beginner course on using AI for everyday tasks helps address the Large Language Models skill gap for the Staff+ Site Reliability Engineer, Safeguards ML Infra role by providing foundational, practical exposure to working with AI tools.
Rust (required)
GapCourse that may help you meet this requirement:
Rust Fundamentals
Coursera · beginner · $49/mo · 40h · ★ 4.0
This course directly covers Rust, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 40 hours.
Incident Response (required)
GapCourses that may help you meet this requirement:
Introduction to Detection and Incident Response
Coursera · beginner · $49/mo · 3h · ★ 5.0
This course directly covers Incident Response, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 3 hours.
Improve your reliability with modern operations practices: Incident response
Microsoft Learn · beginner · Free · 33m · ★ 4.7
This beginner course directly targets the Incident Response skill gap by covering incident and response fundamentals needed for the Staff+ Site Reliability Engineer, Safeguards ML Infra role.
Cyber Incident Response
Coursera · beginner · $49/mo · 2h · ★ 4.7
This course directly covers Incident Response, which appears as a requirement in the Staff+ Site Reliability Engineer, Safeguards ML Infra posting at a beginner level and can be completed in approximately 2 hours.