Anthropic Staff Software Engineer on the GTM AI Engineering team building autonomous agents for go-to-market motions. Responsible for developing evaluation frameworks and setting technical direction for agent systems.
Responsibilities
As a Staff Software Engineer on the GTM AI Engineering (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.
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
Qualification
Production experience with LLMsWorking fluency with dataPassion for advancing safeRepresentative projects(Illustrative of the kind of work
Required
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
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.)
