Technical Program Manager, Storage & Data Infrastructure

OpenAI•San Francisco, CA

3d•$257,000 - $445,000•Hybrid

About The Position

OpenAI's data and storage infrastructure spans data platforms, online databases, and file/object storage. These systems underpin data ingestion and processing, durable persistence, indexing and retrieval, and product file experiences. As frontier models and agents evolve how they use memory, history and snapshots, the underlying architecture increasingly shapes the capabilities products can deliver—and their latency, reliability, cost and efficiency. We are looking for a technically deep TPM to independently define and lead multiple programs across data platforms, online databases and storage infrastructure. You will connect model, product and data-consumer requirements to architecture, and work with the relevant engineering teams to take new capabilities through production adoption and repeatable expansion. The design scope is exabyte-scale storage and infrastructure spanning multiple millions of CPU cores. The challenge is not simply forecasting more resources: it is making complete, workload-ready capacity repeatable, with a clear path from product requirements through architecture, deployment and validation. A data pipeline, database query, file operation or execution snapshot can affect whether a product or agent succeeds; you will connect those outcomes to the systems underneath. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

Requirements

Have independently owned complex production programs in data platforms, databases or storage infrastructure and can explain the architectural decisions, your contribution and the resulting impact.
Have deep working knowledge of hyperscaler/cloud storage technologies, such as Amazon S3 or Azure Blob Storage, including their performance, placement, resiliency and cost constraints.
Understand the full data and storage stack: product access patterns and APIs; ingestion and processing; databases, storage engines and indexes; caching, replication and data movement; and the durable-storage, CPU and network layers that support them.
Can translate model, product and data-consumer needs into precise platform requirements, challenge assumptions and define evidence that a capability, recovery path or scaling change is ready.
Have led delivery across product, model, data, database, storage and infrastructure teams, especially where no single team owns the end-to-end result.
Can use workload evidence to make tradeoffs across capability, correctness, reliability, latency and cost/efficiency, and build mechanisms that improve decisions as requirements change.
Communicate complex decisions clearly and maintain ownership through production adoption, not only a launch milestone.

Responsibilities

Translate model, product and data-platform needs into precise access patterns, consistency, durability, freshness, availability and scalability requirements. Connect memory, history, retrieval and resumable work to capability and end-to-end latency.
Partner with engineering to transform data and storage architecture into repeatable scale units: standardized provisioning, placement, routing, data movement and readiness checks that bring storage, compute and networking online together. Tie each expansion to the workloads it can serve.
Lead cross-stack programs connecting ingestion and processing, databases and indexes, and file/object storage. Make data ownership, schema compatibility, change-data-capture, replay and consumer-readiness contracts explicit so the full data path remains correct and usable.
Make cost and efficiency architectural inputs. Evaluate physical versus logical footprint, index and replication amplification, redundant copies, tiering, caching and network movement against the cost of serving useful workloads.
Drive resilience and recovery programs with explicit failure scenarios and validation. Distinguish database backup, failover and point-in-time recovery from execution/workspace save-and-restore; verify correctness, recovery time, safe resumption and isolation from live traffic.
Coordinate lifecycle correctness across files, objects, databases and data platforms, including metadata, retention, deletion and snapshots. Incorporate privacy, access control, auditability and residency requirements into the design and consumer contracts.
Lead adoption and major migrations through compatibility checks, representative workload testing, staged cutovers, rollback and operational handoff. Improve APIs, guardrails and self-service so new capacity and capabilities can be consumed predictably.
Measure architecture changes through product and platform outcomes: task completion and continuity, data freshness, query/retrieval and snapshot latency, throughput, reliability and cost/efficiency. Use those results to drive durable performance and operational improvements.