OpenAI

Software Engineer, Product - Core Experimentation

Seattle$293k–$324kfulltimemidAdded today

About this role

OpenAI's Statsig team is seeking a senior product engineer to build experimentation, feature rollout, and analytics systems that enable rapid, safe product launches across ChatGPT, Codex, and other core products. You'll work full-stack across frontend, backend, SDKs, and infrastructure to make complex experimentation workflows intuitive for product teams while maintaining performance and reliability at scale.

What you'll do

Design and build product experiences for experimentation, feature flags, dynamic configuration, and rollout safety
Partner with product teams across ChatGPT, Codex, ads, and other areas to translate launch and measurement needs into platform features
Optimize SDK and server-side systems for speed, reliability, and safety in high-scale services
Create workflows that simplify experiment creation, result interpretation, rollout monitoring, and rapid adjustments
Help internal teams adopt and use Statsig effectively across the organization
Contribute to platform technical direction supporting rapid adoption and sophisticated evaluation needs

What they're looking for

Full-stack engineering (frontend, backend, SDK development)
Systems design and infrastructure optimization
Developer experience and API design
Data-informed decision making and analytics
Performance and reliability optimization
Experimentation platform concepts
Cross-functional collaboration and communication
Rapid prototyping and iteration

Benefits

Work on critical infrastructure influencing OpenAI's entire product strategy
Collaborate with high-performing teams across ChatGPT, Codex, and frontier model research
In-person collaboration in Bellevue office
Opportunity to shape the post-acquisition integration of Statsig into OpenAI
Direct impact on how the company learns and launches at scale
Work on systems balancing speed, safety, and rigor

Likely interview questions

Describe a time you improved developer experience or API usability—what made it successful?
How would you approach designing a feature flag system that serves both rapid experimentation and production safety?

Tell us about a complex platform decision where you had to balance performance, reliability, and feature richness. How did you navigate tradeoffs?
Walk us through how you'd partner with a product team to understand their launch and measurement needs, then translate that into platform capabilities.
Statsig was an independent company before joining OpenAI—how would you preserve what made it valuable while deeply integrating it into OpenAI's workflows?
Describe your experience with SDK design, server-side optimization, or infrastructure work at scale. What metrics mattered most?
How do you think about making powerful experimentation concepts feel intuitive to busy product teams who aren't experts in statistics or experimental design?

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