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OpenAI

Full Stack Software Engineer, Growth

San Francisco$153k–$385kfulltimemidAdded 1 month ago

About this role

Join OpenAI's ChatGPT Growth team as a full stack engineer to drive user acquisition and subscription growth through data-driven product optimization. You'll design and deploy backend systems while collaborating across teams to identify high-impact growth opportunities through experimentation and strategic product decisions.

What you'll do

  • Drive ChatGPT growth through data analysis, product ideation, and A/B testing to optimize user experiences
  • Design and deploy backend APIs powering growth-focused product features
  • Optimize user funnels including landing pages, onboarding, and conversion flows
  • Collaborate with research, product, design, and data science teams to execute growth initiatives
  • Track and analyze business metrics and KPIs to inform product decisions
  • Foster an inclusive team culture with radical candor and diverse perspectives

What they're looking for

  • Full stack web development
  • Backend API design and implementation
  • A/B testing and experimental design
  • Data analysis and metrics tracking
  • Product optimization and user funnel design
  • Analytical thinking and scientific approach
  • Rapid learning and knowledge sharing
  • Comfort with ambiguity and changing requirements
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OpenAI

OpenAI builds AI infrastructure and products, including large-scale data center campuses for AI computing and generative AI applications for enterprise customers. The company is hiring civil engineers, project engineers, electrical design engineers, data center R&D engineers, and AI deployment engineers to expand its infrastructure capabilities and help customers deploy AI solutions.

View all jobs at OpenAI

Likely interview questions

  • Tell us about a time you shipped a feature that optimized a user funnel (landing page, checkout flow, etc.). How did you measure success and what was the impact?
  • Describe your experience designing and running A/B tests. What metrics and KPIs have you tracked, and how have you used experimental results to inform product decisions?