Product Operations Manager

OpenAI OpenAI · AI Frontier · San Francisco, CA · Product Operations

This role is for a Product Operations Manager within OpenAI's Applied AI organization. The primary focus is on providing operational expertise to product teams, streamlining org operations, and automating information channels to facilitate collaboration. Responsibilities include managing alpha/beta/GA programs, owning the launch calendar, synthesizing user feedback, coordinating high-impact product moments, and building internal tools/automations. The role requires experience in Product Operations at scaling tech companies and familiarity with AI tools and concepts.

What you'd actually do

  1. Stand up and operate alpha/beta/GA programs with clear entry/exit criteria, recruiting design partners, running internal dogfooding, and transforming qualitative/quantitative insights into prioritized product work.
  2. Own the launch calendar and readiness cadence for a product group; run XFN checkpoints across Product, Eng, Research, Safety, Security, Legal, Support, and GTM.
  3. Build the systems to capture, triage, and synthesize user feedback into actionable recommendations.
  4. Lead coordination for high‑impact moments (e.g., Dev Day demos, rollout waves, partner showcases), managing timelines, resources, and communications to ensure successful outcomes.
  5. Build internal tools/automations (code/no‑code/AI) that scale operations: Feedback intake, issue taxonomy, release notes, etc.Contribute to building and scaling the Product Operations function at OpenAI—codifying best practices, raising the quality bar, and mentoring peers.

Skills

Required

  • Product Operations
  • launch readiness
  • beta programs
  • user feedback synthesis
  • Python
  • JavaScript
  • No-Code/Low-Code Platforms
  • Data Analysis
  • SQL
  • Git
  • GitHub
  • CLIs

Nice to have

  • OpenAI tools and APIs (ChatGPT, Codex, text generation, structured outputs, function calling, etc)
  • Airtable
  • Retool
  • Zapier
  • Google Sheets automations
  • Databricks
  • agentic AI tools
  • LLM-powered agents
  • tool use
  • evals
  • telemetry
  • safety constraints

What the JD emphasized

  • senior IC
  • scaling tech companies
  • build systems/tools (via code / no‑code / AI)
  • technical nuances of LLM‑powered agents