Manager, Applied AI Engineering

Anthropic Anthropic · AI Frontier · San Francisco, CA · Applied AI

Manager of Applied AI Engineers leading a team that advises enterprise customers on adopting and deploying LLM APIs (Claude). Responsibilities include hiring, coaching, setting technical direction, developing evaluation frameworks, and channeling field insights back into product development. Focuses on advanced implementation patterns for LLMs, including prompting and agentic systems.

What you'd actually do

  1. Build, coach, and grow a team of Applied AI Engineers serving Enterprise Tech customers — hiring strong technical builders, developing them through structured feedback and clear competency expectations, and creating scalable onboarding for a customer-facing technical role.
  2. Serve as a senior technical advisor to strategic customers as they deploy new products and workflows on Claude — guiding architecture design, evaluation strategy, and the most advanced prompting, agentic, and implementation patterns from discovery through deployment.
  3. Identify the design patterns, pilots, prototypes, and evaluation suites that recur across engagements, and scale them from individual accounts into segment-wide assets your team can reuse.
  4. Collaborate with Product and Engineering teams to surface field signal, advocate for high-impact product changes, and help shape the roadmap based on what enterprise builders need.
  5. Champion the creation of scalable public and internal assets documenting the latest LLM prompting, evaluation, agentic, and architecture techniques; contribute thought leadership through talks, blog posts, and white papers.

Skills

Required

  • leading and developing technical teams
  • production experience with LLMs
  • advanced prompt engineering
  • agent development and frameworks
  • evaluation frameworks
  • deployment at scale
  • Python or TypeScript programming
  • building production applications
  • engaging senior technical stakeholders
  • influencing technical architecture and product strategy
  • navigating ambiguity
  • cross-organizational collaboration

Nice to have

  • leading technical teams through rapid scale
  • selling or delivering complex technical solutions to large enterprise customers
  • coaching engineers into customer-facing advisory roles

What the JD emphasized

  • production experience with LLMs
  • advanced prompt engineering
  • agent development and frameworks
  • evaluation frameworks
  • deployment at scale

Other signals

  • leading a team of engineers
  • customer-facing technical advisory
  • deploying LLM-powered products
  • shaping product roadmap