Technical Enablement Lead, Claude Platform

Anthropic Limited
San Francisco, CA, United States
about 1 month ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
$270,000.0 - $310,000.0
Working hours
Regular working hours
Job source

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Large Language Models Multi-Agent Systems Production Code

Job description

The Claude Platform ships fast, and every launch lands better when the field can understand it, demo it, and teach it.

You’ll own platform enablement end to end: the demos, hands-on labs, and technical curriculum that let Anthropic’s go-to-market teams showcase the Claude Developer Platform with confidence, whether they’re building on Anthropic’s first-party API or through Bedrock, Vertex, and Foundry. You’ll join roadmap and launch planning with the product team before features ship, so the field has what it needs on day one.

You’re an engineer who teaches. You’ve shipped products to users. You know the difference between a demo that raises awareness and one that creates champions. And you get as much energy from watching a seller nail your lab as from building it yourself., * Turn launches into field-ready content: Translate new platform capabilities (API, tool use and MCP, Agent SDK, memory and skills) into demos, labs, and quick-reference guides within days of release, and build compelling demo scenarios across industries and developer personas

  • Deliver training that sticks: Run Claude Platform training for technical onboarding and ongoing enablement programs, delivering hands-on sessions wherever the field gathers, from a 12-person lab to a 100-person field-readiness day
  • Coach the field: Coach technical sellers on demo delivery through practice sessions and office hours
  • Scale beyond the live session: Develop self-service resources and experimental tools so the field can learn without you in the room, and contribute to the tooling that scales enablement, including demo environments, content-freshness automation, and self-service infrastructure
  • Close the loop with product: Stress-test new platform features before launch, then bring field signal back to product and product marketing on where sellers get stuck and what content would unlock the next deal
  • Measure what lands: Track content usage and session feedback so you know what is working and what to retire

Requirements

  • Experience in a customer-facing technical role, such as solutions architecture, sales engineering, developer relations, or technical enablement
  • Experience writing production-quality code and building technical demos
  • Hands-on experience building with LLM APIs and agents, including tool use, MCP, or agent frameworks, beyond prompting alone
  • Experience using AI coding tools as part of your daily engineering work
  • Experience delivering live technical training or talks to both technical and non-technical audiences
  • Experience partnering directly with sales, solutions engineering, or other go-to-market teams

Preferred requirements

  • A portfolio you can show us: labs, courses, talks, or repos you built yourself
  • Experience with Claude Code, or a comparable agentic coding tool, as core infrastructure in your daily work
  • Experience building on the Claude Developer Platform through cloud providers such as Amazon Bedrock, Google Vertex AI, or Microsoft Foundry
  • Experience as a technical founder, early startup employee, or 0-to-1 operator who has taken an idea to product-market fit
  • Public speaking, conference presentations, or community-facing developer advocacy
  • Experience building enablement tooling, such as demo environments, content-freshness automation, or self-service infrastructure
  • Deep enthusiasm for AI and genuine care about building it responsibly, 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.

Benefits & conditions

Pulled from the full job description

  • Parental leave
  • Flexible schedule, 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.

About the company

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., 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.

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