OpenAI
Software Engineer, Infrastructure - Analytics Platform
- Employment type
- Full-time
- Workplace type
- Hybrid
- Location
- San Francisco
- Published
- Apr 11, 2026
- Last checked
- Oct 26, 2026
About this role
ABOUT THE TEAM
The Platform Analytics team builds the systems OpenAI researchers use to understand the quality and behavior of the models we train including what models are doing, why they behave in a particular way, and how that behavior changes across experiments.
Neptune https://openai.com/index/openai-to-acquire-neptune/?utm_source=neptune-website-redirect is a core part of this work. It ingests, stores, queries, and visualizes large volumes of metrics from pretraining, post-training, and reinforcement learning. Hundreds of researchers depend on these systems in their daily work to compare experiments, debug unexpected behavior, and decide what to try next.
Our scope is broader than metrics. We also build platforms that help researchers analyze samples, traces, evaluation results, and other structured or unstructured data through dashboards, APIs, and increasingly agent-driven workflows. These systems need to remain fast, reliable, and understandable as the scale and complexity of research change quickly.
We are not trying to become a consulting team that builds a separate solution for every research project. We work directly with researchers to understand recurring problems, then turn them into reusable infrastructure and platform capabilities that many teams can build on.
ABOUT THE ROLE
We’re looking for a hands-on experienced software engineer who can take ownership of a critical system and drive it from problem definition through production adoption.
This person should be able to own a platform such as CacheHouse end to end: define its technical direction, design its data model and storage architecture, integrate it with several research dashboards and workflows, guide one or two engineers, and ensure the system works reliably for its users.
The right candidate should already bring the technical judgment, ownership, and execution expected at this level. The primary learning curve should be OpenAI’s stack and research problem space, not learning how to lead a complex engineering effort or deliver a production system.