Anthropic
Applied AI, Research Engineer
About this role
Join Anthropic's Applied AI team as a Research Engineer bridging customer needs with product development. You'll validate model capabilities with real customers, create demos and evaluations, and feed insights back to shape the product roadmap across both technical and go-to-market initiatives.
What you'll do
- Embed with product teams to develop deep expertise on new capabilities and become the field authority in your domain
- Lead technical readiness for model and product launches, including early testing and field assessments
- Build demos, evaluations, and prototypes that showcase customer-relevant capabilities and define performance standards
- Create enablement materials including playbooks, training, and reference architectures for field teams
- Work hands-on with strategic customers on novel technical challenges and package solutions for broader use
- Synthesize customer insights to identify adoption patterns and capability gaps for product and research roadmaps
What they're looking for
- Software engineering with 6+ years experience
- Proficiency in at least one programming language
- Large language model development and integration
- Evaluation and prototype design
- Technical product expertise or forward deployment experience
- Clear communication to diverse stakeholders
- Domain learning and rapid expertise building
- Ambiguity navigation and cross-functional collaboration
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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
- Walk us through a time you built technical enablement or training materials—how did you measure their effectiveness?
- Describe your experience shipping LLM-powered products or integrating models into production systems.