AI Engineer, GTM Claudification

<div class="content-intro"><h2><strong>About Anthropic</strong></h2> <p>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.</p></div><h2>About the role</h2> <p>As an AI Engineer on the GTM Claudification team, you will build the agents and AI systems that run Anthropic's own go-to-market work. Our sellers already work alongside agents every day. You will take things to the next step and build agents that run complete autonomous motions across areas like inbound, outbound, and pipeline management. In addition, you’ll build eval frameworks that prove those agents are ready for customer-facing work and are driving value. This is a senior role where you’ll drive technical direction for agents and evals across our team.</p> <p>Working closely with sellers, RevOps, and our platform engineering partners, you'll own projects from first prototype through production operation. You'll combine full-stack engineering (MCP servers, agentic systems, web applications, etc.) with hands-on evaluation work (behavior benchmarks, production monitoring, ROI measurement), and help architect the shared platforms that builders from across our go-to-market org contribute to. You've worked in cultures of analytical rigor before, and you're eager to help shape the norms and best practices of a growing AI engineering function at a pivotal moment in the company's growth.</p> <h2>Key responsibilities</h2> <ul> <li>Build and operate autonomous agents that run go-to-market motions end to end, across areas like inbound, outbound, pipeline management, and customer engagement</li> <li>Design the human oversight for each motion: approval gates, handoffs, and escalation paths that keep sellers in control</li> <li>Develop evaluation frameworks for agent behavior, and run them in development and in production</li> <li>Instrument model and tool calls in production, and build the observability and measurement that ties agent actions to pipeline and revenue</li> <li>Ship MCP servers, agent skills, and web applications that connect to systems like our CRM, communication tools, and data warehouse</li> <li>Set the technical direction for how we build, evaluate, and operate agents across the team</li> <li>Architect shared codebases that builders from across go-to-market contribute to, setting the conventions and review practices that keep quality high</li> <li>Work directly with sellers to ground agent designs in real workflows, and iterate based on what you observe</li> <li>Identify repeatable patterns and contribute insights back to Anthropic's Product and Engineering teams</li> <li>Maintain strong knowledge of the latest developments in LLM capabilities, agent frameworks, and evaluation techniques</li> </ul> <h2>Minimum qualifications</h2> <ul> <li>Strong programming skills in Python or TypeScript, with experience building and operating production applications</li> <li>Production experience with LLMs, including context engineering, agent development, MCP development, tool use, and evaluation frameworks</li> <li>Experience using evals and transcript analysis to find and fix real problems in an LLM system</li> <li>Working fluency with data, including SQL</li> <li>Ability to navigate ambiguity and ship without a spec, finding simple solutions to complex problems</li> <li>Passion for advancing safe, beneficial AI, and care for the people who use what you build</li> </ul> <h2>Preferred qualifications</h2> <ul> <li>8+ years in roles such as software engineer, ML engineer, or forward deployed engineer. Former technical founders are encouraged to apply</li> <li>Experience with the Claude Code and the Claude Agent SDK</li> <li>Experience with go-to-market systems (CRM, sales engagement, enrichment, conversation intelligence) or time working closely with a revenue team</li> <li>Experience growing a codebase that many people contribute to, inner-source or open-source</li> <li>Applied ML and experimentation background: A/B testing, propensity models, recommendations, or causal analysis</li> <li>Exceptional communication skills to convey technical concepts to non-technical partners with low ego</li> </ul> <h2>Representative projects</h2> <p>(Illustrative of the kind of work, not a project list.)</p> <ul> <li>Build an agent that takes a routine sales workflow from first signal to a drafted, human-reviewed action</li> <li>Stand up the eval suite for an agent: seed scenarios, scoring rubrics, and regression runs on every change</li> <li>Ship an MCP server that gives sellers and their agents governed access to a core revenue system</li> <li>Design a shared repository where go-to-market builders publish agents and skills, with the tests and review rules that keep it healthy</li> <li>Build a predictive model that explains itself, so an agent can tell a seller why it suggests an action</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>The annual compensation range for this role is listed below.&nbsp;</p> <p>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.</p></div><div class="title">Annual Salary:</div><div class="pay-range"><span>$320,000</span><span class="divider">&mdash;</span><span>$405,000 USD</span></div></div></div><div class="content-conclusion"><h2><strong>Logistics</strong></h2> <p><strong>Minimum education: </strong>Bachelor’s degree or an equivalent combination of education, training, and/or experience</p> <p><strong>Required field of study:&nbsp;</strong>A field relevant to the role as demonstrated through coursework, training, or professional experience</p> <p><strong>Minimum years of experience: </strong>Years of experience required will correlate with the internal job level requirements for the position</p> <p><strong>Location-based hybrid policy:</strong> 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.</p> <p><strong data-stringify-type="bold">Visa sponsorship:</strong>&nbsp;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.</p> <p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong> Not all strong candidates will meet every single qualification as listed.&nbsp; 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.<br><br><strong data-stringify-type="bold">Your safety matters to us.</strong> To protect yourself from potential scams, remember that Anthropic recruiters only contact you from&nbsp;@anthropic.com&nbsp;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&nbsp;<u data-stringify-type="underline"><a class="c-link c-link--unde