# Software Engineer, Compute Infrastructure

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA, New York, NY, Seattle, WA
**Role:** DevOps / SRE
**Salary:** $230k – $405k/yr
**Skills:** Kubernetes, Nccl, Rdma, Distributed Systems, High-Performance Computing, Gpu Infrastructure, Observability, Reliability Engineering, Storage Systems, Networking Protocols
**Posted:** 2026-04-27

> Builds and optimizes large-scale compute infrastructure for AI workloads, spanning hardware automation, distributed systems, Kubernetes orchestration, networking, storage, and developer tools. Requires strong systems engineering experience in performance, reliability, and production infrastructure.

## Job Description

## Responsibilities
- Build and deeply optimize reliable system software for large-scale compute systems that run some of the world's most demanding AI workloads
- Design and operate infrastructure across accelerators, CPUs, NICs, switches, networking protocols, storage, data centers, cluster orchestration, scheduling, and fleet health
- Profile, benchmark, and optimize training workloads across compute, memory, storage, networking, NCCL and collective communication, and cluster scheduling bottlenecks
- Create hardware-aware automation that makes provisioning, firmware and driver upgrades, incident response, and day-to-day operations faster and less error-prone
- Build CaaS, agent infrastructure, profiling, observability, benchmarking, and platform tools that help researchers, product engineers, and operators launch, debug, and optimize workloads with less friction
- Turn operational lessons into better systems, stronger abstractions, and clearer ownership boundaries across teams
- Collaborate across research, engineering, security, networking, hardware, and data center teams to make compute capacity more capable and easier to use

## Qualifications
- Strong software engineering skills and experience building, operating, or improving production infrastructure systems
- Experience in one or more relevant areas such as distributed systems, operating systems, networking protocols, **RDMA**, **NCCL** or collective communication, storage, **Kubernetes**, scheduling, observability, reliability engineering, high-performance computing, GPU infrastructure, CaaS, agent infrastructure, hardware-aware performance optimization, benchmarking, developer experience, or infrastructure tooling
- Ability to debug complex system behavior across software, hardware, networking, and workload layers, then turn findings into robust improvements
- Comfort with ambiguity, strong ownership, and a bias toward practical, durable solutions

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