Staff+ Software Engineer, Safeguards ML Infrastructure

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

Job summary

Doist is seeking an experienced SRE/Platform Engineer to design, build, and operate the Safeguards ML Infra backend that powers Claude safety systems. You will own production services across 1P, AWS Bedrock, and GCP Vertex, defining SLOs, observability, and incident response for critical production paths.

You will work closely with ML researchers to productionize safety techniques, implement automated validation, and build self‑serve tooling to reduce toil, while acting as a real‑world user of

Qualifications

• Proficient in Python; experience with Rust is a plus.
• Designed, built, and operated high‑QPS systems at global scale.
• Strong foundation in distributed systems: replication, consistency, failure modes, and SLO management.
• Meaningful on‑call experience for production systems, including incident response and post‑mortem improvements.
• Hands‑on experience deploying and operating on cloud platforms (AWS, GCP) at scale.

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 on‑call and on‑duty 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.

Skills

Python

Rust (optional)

Distributed systems

On-call experience

Cloud platforms

Tools

AWS

GCP

Job description

About the role

The Safeguards ML Infra team designs, builds, and operates the production infrastructure that powers Claude's safety systems. We own both the critical backend services that ensure safety on the token generation path, as well as the infrastructure that configures these systems during model provisioning for every platform Claude runs on – 1P, Bedrock, Vertex, and beyond. We define and maintain SLOs, build the observability systems that surface problems early, and lead incident response when issues arise.

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 on‑call and on‑duty 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.

Qualifications

• Proficient in Python; experience with Rust is a plus but not required.
• 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 post‑mortem‑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.

Preferred Experience

• 8+ years of industry software engineering experience.
• Building deployment and rollout systems with canary analysis, automated validation, or progressive rollout controls.
• 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.

Compensation

Annual Salary: $320,000 — $485,000 USD.

Location

Hybrid: staff are expected to be in one of our offices at least 25% of the time, though some roles may require more time in office.

Visa Sponsorship

We sponsor visas and will make every reasonable effort to obtain a visa if you receive an offer.