Customer Success Programs Manager

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.

At Anthropic, we believe the next generation of Customer Success looks fundamentally different; most customer outcomes will be delivered through programs, not 1:1 relationships, and increasingly without a human in the loop. As a Success Programs Manager, you'll own a portfolio of those programs and flex across whatever the function needs to drive adoption and value at scale.

As a CS Programs Lead you’ll think "How could we do this with Claude?" as a reflex — your default is to build an agent or an automated journey before you build a manual workflow. But you're also fluent in the craft of running engagements: you've personally designed and delivered 1:many webinars, stood up onboarding cohorts, and built communities that compound. You move comfortably between shipping an AI-native lifecycle flow on Monday and facilitating a live customer cohort on Tuesday.

You'll work across the full Claude product surface, designing and shipping the programs that take customers from activation to value realization, expansion, and renewal. Instead of managing a book of accounts, you'll manage a portfolio of programs, each one a compounding asset that serves more customers, more effectively, every week it ships. You hold a high bar for measurable impact, you instrument what you build, and you retire what doesn't earn its keep.

If the idea of a CS team that builds and ships as much as it joins calls excites you, and you want the range to do both, this role is for you.

• Build and run a portfolio of programmatic CS plays (activation, scale and expand) across the long tail and unmanaged segments, spanning Claude Enterprise; Cowork, and Claude Code.

• Design and ship Claude-powered engagement plays that replace or augment traditional CSM touchpoints: use-case discovery chats, digital QBRs, health reviews, feature nudges, consumption-drop saves, and expansion prompts. Define entry criteria, agent behavior, exit criteria, and success metrics for each.

• Design and deliver high-leverage live engagements. 1:many webinar series, onboarding cohorts, customer communities, and academies, and look for every opportunity to make them AI-native, repeatable, and self-serve over time.

• Flex across the needs of the function. Some weeks the priority is an agent; some weeks it's a cohort or a community launch. You bring comprehensive knowledge of what effective CS programs look like and apply the right model to the problem in front of you.

• Instrument every program with consumption, product telemetry, and qualitative signals. Know which touchpoints — digital or live — deliver the most value and where the handoff between digital and human should sit, and invest accordingly.

• Treat every cohort as an experiment. Continuously iterate on agent prompts, workflow logic, content, facilitation, and channel mix. Hold a high bar for measurable impact; kill plays that don't move the numbers.

• Represent the customers a human will never meet. Synthesize patterns from thousands of program interactions and channel them to Product, Marketing, and Education so repeat issues get solved once.

• Partner with Scaled CSMs, Sales, Strategy & Operations, and Support to define the rules of engagement: where programmatic graduates to human, where human hands back to programmatic, and how the modes reinforce rather than duplicate each other.

• Model what Claude-native CS looks like and help the rest of the CS org get there — your ratio of things-you-wrote to things-you-shipped-with-Claude should tilt hard toward the second.

• 6-8+ years in Customer Success, with meaningful time in a Digital, Scaled, or Programmatic CS function.

• A clear track record of delivering measurable customer outcomes; activation, adoption, NRR, retention, without a dedicated 1:1 relationship.

• You've shipped lifecycle programs, in-app flows, digital QBRs, academies, webinar series, community programs, or churn-save automations that moved real numbers.

• Hands-on fluency with AI in your own workflow. You've prototyped agents, generated content, analyzed accounts, or replaced internal processes with LLMs and you can talk concretely about what worked, what didn't, and what's next. You don't wait for AI tooling to arrive; you build it.

• Direct experience running live 1:many engagements. Webinar series, onboarding cohorts, communities, or academies and the instinct to make them more AI-native and repeatable every time you run them.

• Comprehensive knowledge of effective CS programs and the range to flex across them. You know the strengths and failure modes of tech-touch, pooled, 1:many, and digital models, and you pick the right one for the problem rather than defaulting to the one you know best.

• A restless "how could we do this with Claude?" reflex. When you see a manual workflow, your first instinct is to replace it with an agent. When you see a 1:1 touchpoint, you ask whether it could be 1:many or pure digital.

• Strong data instincts. You're comfortable analyzing trends, reading consumption dashboards, and translating product telemetry into triggers. SQL or lightweight scripting is a plus.

• Technical literacy with API-first and developer-facing products. You can follow a Claude Code workflow, reason about token economics, and have a credible product conversation with technical customers and PMs.

• Excellent written communication. Most of your output is customer-facing copy, prompts, agent instructions, facilitation guides, and playbooks. Tone, clarity, and specificity matter.

• Conviction about responsible AI deployment and genuine interest in Anthropic's mission.

$260,000—$260,000 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 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.

How we're different

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.