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
Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users — demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand.
Developer Productivity owns the end-to-end experience of how engineers and researchers at Anthropic develop, build, test, and ship code at scale. This role focuses on the configuration and experimentation systems that let teams safely roll out changes, run controlled experiments, and make data-driven decisions across our products and infrastructure — from feature flagging and dynamic configuration to A/B testing platforms that support both research and production workloads.
Responsibilities
- Own the technical strategy and roadmap for config and experimentation infrastructure, translating team-level goals into concrete execution plans and partnering with teams focused on deployment, testing, and more
- Maintain and enhance the architecture for feature flagging, dynamic configuration, and experimentation systems, ensuring the hardest problems get solved — whether by you directly or by working through others
- Design and build scalable, reliable distributed infrastructure and shared libraries that support high-volume experimentation and config workloads across all engineering teams
- Own and evolve the platforms and tooling that let engineers and researchers safely ship config changes, run experiments, and measure impact
- Define standards, tooling, and frameworks for experimentation and configuration management that drive developer productivity across research and production workloads
You may be a good fit if you:
- Have deep experience with configuration management, feature flagging, and/or experimentation platforms in a large-scale environment
- Have strong proficiency in Python, Rust and/or Go
- Are obsessed with developer productivity and reducing friction in how teams configure, test, and ship changes
- Have experience with container orchestration and infrastructure at scale
- Have excellent communication skills and enjoy supporting internal partners to improve their development experience
- Are excited about designing foundational systems and are comfortable working independently on ambiguous, high-impact technical challenges
Strong candidates may have:
- Have 15+ years (not including internships or co-ops) of experience in a Software Engineer role, building and operating large-scale developer infrastructure
- Have 3+ years (not including internships or co-ops) of experience leading large scale, complex projects or teams as an engineer or tech lead
- Experience with config or experimentation platforms such as Statsig, GrowthBook, LaunchDarkly, Optimizely, Unleash, or similar (including building such systems in-house)
- Experience designing experimentation frameworks — randomization, assignment, metrics pipelines, and statistical analysis for A/B testing at scale
- Experience with dynamic configuration systems and safe rollout mechanisms (gradual rollouts, kill switches, targeting rules)
- Experience building CLI tools, developer-facing services, and APIs/automation workflows that integrate config and experimentation into existing CI/CD pipelines
Deadline to apply: None. Applications will be reviewed on a rolling basis.
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.
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.
Anthropic San Francisco, California, USA Office
548 Market St, San Francisco, California, United States, 94104
Similar Jobs
What you need to know about the San Francisco Tech Scene
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
