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← openai / Product Manager, API Infrastructure

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role
openai / Product Manager, API Infrastructure
model
anthropic/claude-sonnet-4.6
created
2026-05-28T04:18

Company snapshot

OpenAI is the leading AI research and deployment company behind GPT-4, o-series reasoning models, DALL-E, Sora, and the ChatGPT consumer product. The API platform serves millions of developers and enterprises globally, generating substantial revenue through usage-based pricing. In the last 12–24 months OpenAI has aggressively expanded enterprise offerings (custom data residency, SSO/SCIM, spend controls), launched the Assistants and Batch APIs, and introduced fine-tuning and structured outputs endpoints — all of which increase the surface area and complexity of the API infrastructure PM role. The company has also faced intense scrutiny around data privacy and model safety, making enterprise-grade data governance a board-level priority. Engineering reputation is strong but fast-moving; the API team is known for shipping at high velocity while managing extreme reliability requirements (millions of concurrent API calls).

Team stack

Based on the JD and public signals: API gateway and model-serving layer likely built on internal Go/Python microservices (likely, based on industry norms and Splunk/Intuit parallels in JD signals); billing and metering likely on Stripe or a custom usage-metering system with event streaming (Kafka or similar, likely); data governance and audit logging likely backed by a combination of cloud-native storage (S3/GCS) with encryption-at-rest and a metadata catalog; identity/access management via SAML 2.0/OIDC, SCIM provisioning, likely integrated with Okta or similar IdP (based on JD mention of SSO/SAML, SCIM); regional data processing footprint suggests multi-cloud or multi-region deployment (AWS + Azure at minimum, based on JD); dashboards and cost-visibility tooling likely custom-built on top of internal telemetry pipelines; compliance and audit tooling likely intersects with SOC 2 Type II, GDPR, HIPAA controls (inferred from 'high-trust enterprise' language in JD).

Likely questions (10)

areaquestionwhy
system_design Design a usage metering and cost-visibility system for the OpenAI API that handles millions of API calls per day, supports per-organization spend limits, real-time alerts, and predictable billing. Walk through the data pipeline, storage, and API surface. JD explicitly calls out 'usage metering, cost dashboards, alerts, budgeting tools, and predictable API cost experiences' as core deliverables.
system_design How would you design an enterprise data control plane that allows customers to enforce data residency (e.g., EU-only processing), manage encryption keys, and audit all data access — without degrading API latency? JD leads with 'enterprise data control plane' and 'expanding regional data processing footprint' as primary strategic bets.
domain Walk me through how you would define and prioritize the roadmap for SSO/SAML, SCIM provisioning, and role-based access controls for an enterprise API platform. What are the hardest tradeoffs? JD specifically calls out 'access control models, identity flows (SSO/SAML, SCIM), and admin tooling' as a core responsibility.
domain What is your framework for thinking about inference caching controls in an API context — what data governance, privacy, and cost tradeoffs does a PM need to navigate when exposing cache-hit/miss controls to developers? JD explicitly names 'enabling new inference caching controls in the API' as an example project.
behavioral Tell me about a time you drove alignment across engineering, legal, compliance, and finance on a high-stakes platform decision. What was the decision, who pushed back, and how did you resolve it? JD emphasizes 'partners deeply with engineering, security, legal, compliance, finance and leadership' and 'drive alignment across complex technical and regulatory environments.'
behavioral Describe a 0-to-1 developer platform product you shipped. What was the hardest part of defining the MVP, and how did you measure success post-launch? JD requires proven experience with developer-facing infrastructure products; candidate's Intuit ICE Self-Service and SDK work are directly relevant.
coding You need to build a SQL query (or BigQuery pipeline) to detect anomalous API spend for an enterprise customer — define the schema, the query logic, and how you'd surface alerts. What edge cases matter most? JD calls out analytical skills and cost-management tooling; candidate's Intuit experience with SQL/BigQuery telemetry is a direct signal the interviewer will probe.
culture OpenAI moves extremely fast and ships under significant public scrutiny. How do you balance speed-to-ship with the rigor required for enterprise data privacy and compliance features? Give a concrete example. JD language around 'high-risk, high-trust areas' and OpenAI's public profile around safety/privacy make this a culture-fit litmus test.
domain How would you approach building a data retention and lifecycle management capability for the API — covering training data opt-outs, prompt/completion storage windows, and audit log retention — in a way that satisfies GDPR, CCPA, and enterprise contractual requirements simultaneously? JD explicitly lists 'retention, encryption, audit logs, permissions, and lifecycle management' under data governance capabilities.
behavioral Tell me about a time you used quantitative data (usage telemetry, SQL, dashboards) to make a prioritization decision that was counterintuitive or unpopular with stakeholders. JD emphasizes 'strong systems thinking, analytical skills'; candidate's Intuit work with BigQuery/SQL to prioritize developer pain points across 20+ apps is directly testable here.

Talking points