ML Infrastructure Engineer, Safeguards

Responsibilities

• Design, build, and deploy backend services that are critical safety pieces on the token sampling and generation path.
• Own and operate the production serving infrastructure for those services across multiple deployment platforms (1P, AWS Bedrock, GCP Vertex).
• Define and maintain SLOs, build observability and alerting systems, and lead incident response for infrastructure on the critical path of every Claude request.
• Participate in on-call and operational-duty rotations covering service incidents, model provisioning, and time-sensitive research and safety launches.
• Reduce oncall and onduty toil by building automation, tooling, and self-serve workflows that minimize manual operations. Be the first user of the systems you build, running them for real workloads yourself before other teams depend on them.
• Build and maintain a safety registry with full provenance -- tracking what is running in production, on which model, and when and by whom it was deployed.
• Implement automated post-deploy validation to ensure correctness is consistent across platforms.
• Work closely with ML researchers to productionize new safety techniques, translating experimental work into reliable, scalable production systems.
• Contribute to the long-term goal of platform-agnostic deployment tooling that brings 3P platforms to parity with 1P operational maturity.

Requirements

• Proficient in Python.
• Designed, built, and operated high QPS systems at global scale.
• Strong foundation in distributed systems: replication, consistency tradeoffs, failure modes, and SLO management under load.
• Meaningful on-call experience for production systems, including incident response and postmortem-driven improvements.
• 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.
• Hands-on experience deploying and operating on cloud platforms (AWS, GCP) at scale.
• Approach infrastructure as a platform -- building systems and abstractions that other engineers build on, rather than point solutions for a single team's needs.
• Bachelor’s degree or an equivalent combination of education, training, and/or experience in a relevant field.

Nice-to-Haves

• Experience with Rust.
• 8+ years of industry software engineering experience.
• Experience building deployment and rollout systems with canary analysis, automated validation, or progressive rollout controls.
• A demonstrated history of reducing operational toil through automation, including transitioning teams from manual deployment processes to self-serve pipelines.
• Familiarity with LLM inference systems and the operational characteristics of transformer-based models.