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Cognition

Applied AI Engineer

San Francisco$180k–$225kfulltimemidAdded 1 month ago

About this role

Join Cognition as an Applied AI Engineer to deploy Devin, an AI software engineer, directly into enterprise teams. You'll drive adoption of agentic workflows, measure impact, and help scale best practices across the organization while working with world-class engineers in San Francisco.

What you'll do

  • Embed with enterprise engineering teams to drive deep adoption of Devin and measure productivity outcomes
  • Design and implement agentic workflows across engineering, QA, support, and data functions
  • Conduct interactive workshops and pair programming sessions with customer teams
  • Configure, optimize, and troubleshoot Devin deployments and associated tools
  • Quantify business impact through metrics and ROI storytelling to expand account footprint
  • Develop scalable playbooks and enablement materials from customer learnings

What they're looking for

  • Software engineering with Python, JavaScript/TypeScript, or similar languages
  • Technical consulting or solutions engineering experience
  • Ability to communicate complex technical concepts to diverse audiences
  • Track record of driving adoption and measurable impact in engineering organizations
  • LLM or agent-based systems deployment experience
  • Developer enablement or platform adoption leadership
  • Commercial acumen and business judgment
  • Fast learning and adaptability
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Cognition

Cognition builds AI software engineers and developer tools, including Devin (an AI software engineer) and Windsurf (an AI-native IDE) that help developers automate tasks and write code more efficiently. The company is hiring Deployed Engineers to work directly with customers on adoption and integration, SREs to manage production reliability and infrastructure, federal engineers for government deployments, and IT specialists to support internal operations.

View all jobs at Cognition

Likely interview questions

  • Tell us about a time you embedded with an engineering team to drive adoption of a new tool or technology. How did you measure success, and what metrics improved?
  • Describe your experience deploying or integrating LLM or agent-based systems in production. What were the key challenges and how did you overcome them?
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