Staff Software Engineer, Developer Productivity (CI/CD) - Claude Code

Job Application for Staff Software Engineer, Developer Productivity (CI/CD) - Claude Code at Anthropic

Back to jobs New Staff Software Engineer, Developer Productivity (CI/CD) - Claude Code San Francisco, CA | New York City, NY

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

Every engineer at Anthropic depends on the path from pull request to production. The Developer Productivity team owns that path end to end — review automation, CI, the merge queue, the deploy pipeline, and the policy that gates each step. These pieces exist today; the opportunity is to integrate them into a single fast, predictable system that scales with the volume of code shipping into Claude and our research infrastructure.

In this role, you'll be responsible for making "time from push to healthy in production" a metric the whole company can rely on. You'll shape the CI and repository topology that best serves our velocity, build AI-assisted review that keeps confidence high as PR volume grows, and partner closely with platform, security, and delivery infrastructure teams on the substrate underneath. This is a tech-lead-scope IC role with broad cross-team influence — you'll represent Developer Productivity in org-wide pipeline decisions and help other teams adopt the standards you set.

We use Claude heavily in our own development workflows, and this team is at the center of that: agentic coding is both how we work and part of what you'll be building.

Key responsibilities

Own the build, test, merge, and deploy pipeline end to end — what runs on each PR, what auto-approves, what gates merge, and how a change progresses to running healthy in production

Drive down and defend "time from push to healthy in prod" as a core engineering metric

Design and tune AI-assisted code review so confidence-to-land scales with PR volume

Build the deploy and release path — canary, progressive rollout, health checks, automated rollback — in partnership with the platform teams who own the underlying substrate

Improve test reliability by quarantining, root-causing, and retiring intermittent failures

Shape CI and repository topology (build graph, test targeting, scope boundaries) to match how the company actually ships

Partner with platform, delivery infrastructure, and security teams, and represent Developer Productivity in cross-org pipeline decisions

Design processes (postmortem review, incident response, on-call) that help the team operate reliably and never fail the same way twice

Minimum qualifications

Significant backend or developer-infrastructure engineering experience, with hands-on responsibility for a high-leverage CI/CD, merge queue, or land pipeline at scale

Proficiency in Python and at least one statically-typed systems language (e.g., Go or Rust)

Experience operating CI/CD or release systems through production incidents, including writing postmortems and driving remediations

Demonstrated ability to work across team boundaries — building consensus with platform, security, and product engineering stakeholders

Comfort using AI coding tools as a daily part of your workflow, with informed opinions on where they provide leverage

Preferred qualifications

7+ years of backend or developer-infrastructure experience

Experience with Bazel or similar build-graph / test-targeting systems at monorepo scale

Experience with progressive delivery or release engineering at scale (canary analysis, automated rollback, health-gated promotion)

A track record of leading — or making the well-reasoned case against — a repo split, monorepo extraction, or comparable scope-boundary migration

A history of authoring engineering policy or paved-path tooling that other teams adopted voluntarily

Familiarity with Kubernetes, Buildkite, GitHub Actions, or comparable CI/deploy substrates

Interest in the safe and beneficial development of AI

Representative projects

Reducing p50 merge-to-production time by re-architecting the merge queue and test selection strategy

Building an AI-assisted review layer that auto-approves low-risk changes and routes high-risk ones to the right reviewers

Designing a flaky-test quarantine and burndown system that returned CI signal to >99% reliability

Standing up canary and progressive rollout for a service fleet, with automated rollback on health regression

Authoring the RFC and migration plan for a build-graph or repository topology change adopted across multiple teams

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...