AI Engineer, GTM Claudification

About the role

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.

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.

Key responsibilities

• Build and operate autonomous agents that run go-to-market motions end to end, across areas like inbound, outbound, pipeline management, and customer engagement
• Design the human oversight for each motion: approval gates, handoffs, and escalation paths that keep sellers in control
• Develop evaluation frameworks for agent behavior, and run them in development and in production
• Instrument model and tool calls in production, and build the observability and measurement that ties agent actions to pipeline and revenue
• Ship MCP servers, agent skills, and web applications that connect to systems like our CRM, communication tools, and data warehouse
• Set the technical direction for how we build, evaluate, and operate agents across the team
• Architect shared codebases that builders from across go-to-market contribute to, setting the conventions and review practices that keep quality high
• Work directly with sellers to ground agent designs in real workflows, and iterate based on what you observe
• Identify repeatable patterns and contribute insights back to Anthropic's Product and Engineering teams
• Maintain strong knowledge of the latest developments in LLM capabilities, agent frameworks, and evaluation techniques

Minimum qualifications

• Strong programming skills in Python or TypeScript, with experience building and operating production applications
• Production experience with LLMs, including context engineering, agent development, MCP development, tool use, and evaluation frameworks
• Experience using evals and transcript analysis to find and fix real problems in an LLM system
• Working fluency with data, including SQL
• Ability to navigate ambiguity and ship without a spec, finding simple solutions to complex problems
• Passion for advancing safe, beneficial AI, and care for the people who use what you build

Preferred qualifications

• 8+ years in roles such as software engineer, ML engineer, or forward deployed engineer. Former technical founders are encouraged to apply
• Experience with the Claude Code and the Claude Agent SDK
• Experience with go-to-market systems (CRM, sales engagement, enrichment, conversation intelligence) or time working closely with a revenue team
• Experience growing a codebase that many people contribute to, inner-source or open-source
• Applied ML and experimentation background: A/B testing, propensity models, recommendations, or causal analysis
• Exceptional communication skills to convey technical concepts to non-technical partners with low ego

Representative projects

(Illustrative of the kind of work, not a project list.)

• Build an agent that takes a routine sales workflow from first signal to a drafted, human-reviewed action
• Stand up the eval suite for an agent: seed scenarios, scoring rubrics, and regression runs on every change
• Ship an MCP server that gives sellers and their agents governed access to a core revenue system
• Design a shared repository where go-to-market builders publish agents and skills, with the tests and review rules that keep it healthy
• Build a predictive model that explains itself, so an agent can tell a seller why it suggests an action