Anthropic
Recruiting Solutions Engineer
About this role
Anthropic seeks a Recruiting Solutions Engineer to be the technical bridge between their recruiting team and Claude AI capabilities. You'll educate recruiters on AI workflows, provide hands-on technical support, and build fast prototypes that advance how the company hires—blending enablement, support, and product development.
What you'll do
- Serve as technical advisor embedded with recruiting teams to expand Claude's use in hiring workflows
- Identify, codify, and teach recruiter-created skills and workarounds through workshops and office hours
- Pair hands-on with recruiters on prompts, skills, and agent workflows; convert support patterns into documentation
- Build fast prototypes and pilots for high-value recruiting workflows with a path to production handoff
- Collaborate with AI governance and People Products teams to vet and productionize recruiter-built tools
- Create technical content for non-engineer audiences: documentation, tutorials, and sample skills
What they're looking for
- LLM application development and production experience
- Prompting and context engineering
- Agent architecture design
- Python or TypeScript programming
- Technical teaching and enablement
- AI evaluation methodologies
- Internal tools and rapid prototyping
- Cross-functional communication
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Anthropic
Anthropic builds Claude, an AI assistant, and is hiring for engineering roles across infrastructure, data systems, and security that support both AI research operations and the company's internal technology needs. The company seeks infrastructure engineers, systems integrators, data scientists, and security specialists to build production-scale systems for training data pipelines, financial operations, developer productivity measurement, research infrastructure, and server firmware security.
- Website
- anthropic.com
Likely interview questions
- Tell us about a time you shipped a quick prototype or MVP when a more perfect solution would have taken much longer—how did you decide it was the right call?
- Describe your experience building LLM-powered applications in production. What were the trickiest parts of prompting or context engineering you've solved?