at Anthropic
Location
San Francisco, CA | New York City, NY
Compensation
$405k–$485k USD
Type
full time
Posted
5 days ago
Remote
Yes
Market range · company + function + seniority
p25 · target · p75 · n=44
Posted $485k · in the market band
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Free account · ATS keyword check · per-job bullet rewrite by Claude.
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.
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.
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
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
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
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.
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.
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.
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.
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