Software Engineer, Infrastructure - Analytics Platform

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.

Responsibilities

Own critical systems across architecture, implementation, integrations, deployment, monitoring, adoption; 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; Address the largest bottleneck first

Requirements

Hands-on experienced software engineer who can take ownership of a critical system and drive it from problem definition through production adoption; Ability to own a platform such as CacheHouse end to end; Technical judgment, ownership, and execution expected at this level; Ability to work directly with researchers and engineers; Ownership of a critical distributed system or research platform end to end; Experience with high performance programming languages (Rust or C++), including performance profiling, concurrency, async execution, memory behavior, serialization, I/O, networking, and failure analysis; Deep expertise in at least one relevant area such as distributed databases, storage engines, analytics systems, telemetry, logging, search, ingestion, or query execution; Ability to reason carefully about partitioning, replication, consistency, retries, backpressure, event lineage, duplicate or delayed data, overload, migrations, and failure isolation; Experience with ClickHouse or similar OLAP, columnar, time-series, or high-throughput analytical systems; Strong computer science fundamentals and algorithmic reasoning; Regular use of coding agents and opinions about how AI tools change software development; Ability to work directly with researchers and other highly technical users; Judgment to focus on the largest practical bottleneck; Strong engineering skills; Clear communication and business judgment

Nice to have

Direct ClickHouse experience; Competitive-programming experience