Software Engineer, Infrastructure - Analytics Platform

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 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.

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.

You will work directly with researchers and the engineers supporting them. You’ll need to understand what users are actually trying to accomplish, distinguish the underlying problem from a proposed feature, and translate recurring needs into simple, durable platform components.

• Own critical systems across architecture, implementation, integrations, deployment, monitoring, adoption, and whatever else is required to make them useful.

• Design, build, and operate the ingestion, storage, retrieval, and query systems behind research analytics at OpenAI.

• Build platforms for analyzing both structured metrics and unstructured research data, including samples, traces, evaluation results, and model-behavior data.

• Reason below typical service abstractions about algorithms, data structures, concurrency, storage layout, distributed systems, query performance, and failure modes.

• Make architectural tradeoffs across ingestion-time processing, data models, storage formats, indexing, query execution, caching, and visualization performance.

• Work directly with researchers to understand what they are trying to learn, ask the right questions, and challenge a requested solution when a simpler or more effective approach exists.

• Recognize when several teams are solving the same problem and turn those patterns into shared infrastructure, APIs, platform components, or agent-driven workflows.

• Write code, investigate production issues, make realistic estimates, guide other engineers, and think through testing, rollout, observability, and rollback.

• Keep solutions as simple as the problem allows. Introduce complexity only when correctness, scale, latency, or reliability genuinely require it, and address the largest bottleneck first.
  

• Have owned a critical distributed system or research platform end to end and can explain how you took it from an ambiguous problem to reliable production adoption.

• Have experience with high performing programing languages (Rust or C++), including performance profiling, concurrency, async execution, memory behavior, serialization, I/O, networking, and failure analysis.

• Bring deep expertise in at least one relevant area such as distributed databases, storage engines, analytics systems, telemetry, logging, search, ingestion, or query execution.

• Can reason carefully about partitioning, replication, consistency, retries, backpressure, event lineage, duplicate or delayed data, overload, migrations, and failure isolation.

• Have experience with ClickHouse or similar OLAP, columnar, time-series, or high-throughput analytical systems. Direct ClickHouse experience is helpful but not required.

• Bring strong computer science fundamentals and algorithmic reasoning. Competitive-programming experience is welcome but not required.

• Already use coding agents regularly and have opinions about how AI tools change software development, product interfaces, and engineering leverage.

• Enjoy working directly with researchers and other highly technical users: understanding their constraints, debugging ambiguous problems, explaining tradeoffs, and closing the loop after shipping.

• Have the judgment to focus on the largest practical bottleneck rather than expanding every project into a complete platform rewrite.

• Are a strong engineer first. Clear communication and business judgment are non-negotiable, but your credibility comes from building and operating systems that work.
  

Researchers need reliable ways to understand model quality, behavior, and reasoning across frontier training and evaluation workflows. When analytics are slow, unavailable, or unable to express a new question, research slows with them.

The systems you build will help researchers move from raw metrics, samples, traces, and model outputs to useful understanding. Better latency, reliability, and reusable platform capabilities can compound across hundreds of researchers and many of OpenAI’s most important research efforts.

About OpenAI

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