OpenAI의 Compute Infrastructure 팀에서 AI 연구와 제품을 뒷받침할 대규모 컴퓨팅 플랫폼을 설계하고 최적화할 엔지니어를 찾습니다. 분산 시스템, 고성능 컴퓨팅, 네트워킹 또는 인프라 자동화 분야의 전문성을 갖춘 분을 환영합니다. 하드웨어부터 소프트웨어까지 전체 스택을 아우르며, 복잡한 시스템 문제를 해결하고 연구 생산성을 높이는 핵심적인 역할을 수행하게 됩니다.
Compute Infrastructure builds the platform that turns enormous amounts of compute into a reliable engine for frontier AI. We design, provision, schedule, operate, and optimize the systems that connect accelerators, CPUs, networks, storage, data centers, orchestration software, agent infrastructure, developer tools, and observability into one coherent experience for researchers and product teams.
Our work spans the entire stack: capacity planning and cluster lifecycle, bare-metal automation, distributed systems, Kubernetes and scheduling, deep system optimization, high-performance networking, storage, fleet health, reliability, workload profiling, benchmarking, and the developer experience that lets teams use enormous compute systems with confidence. At this scale, small improvements to communication, scheduling, hardware efficiency, or debugging workflows can compound into meaningful research velocity. We are hiring across Compute Infrastructure rather than for a single narrow team, and we use this opening to match strong engineers to the problems where they can have the most leverage.
We are looking for engineers who want to build the compute platform behind OpenAI's research and products. You may be strongest in low-level systems, high-performance computing, distributed infrastructure, reliability, CaaS, agent infrastructure, developer platforms, tooling, or the user experience around infrastructure. What matters is that you can reason carefully about complex systems, write durable software, and raise the quality and velocity of the people around you.
Depending on your background and interests, you might work close to hardware, close to users, on CaaS and agent infrastructure, or on the control planes and data planes in between. You could help bring new supercomputing capacity online, optimize training workloads from profiler traces and benchmarks, improve NCCL and collective communication behavior, reason about GPUs, NICs, topology, firmware, thermals, and failure modes, or design abstractions that make heterogeneous clusters feel like one coherent platform.
We do not expect every candidate to have worked at every layer. Some engineers will go deep on systems performance, kernel or runtime behavior, large-scale networking protocols, RDMA, NCCL, GPU hardware behavior, benchmarking, scheduling, or hardware reliability; others will make the platform more usable through APIs, tools, workflows, and developer experience. The common thread is strong engineering judgment and excitement about making enormous compute systems faster, more reliable, and easier to use.
This is a general opening for Compute Infrastructure. We will consider candidates for teams across Compute Infrastructure and match you based on your strengths, the problems that motivate you, and where the infrastructure needs are highest.
Compute Foundations: Build the low-level platform primitives that make heterogeneous hardware, providers, and data centers repeatable, automatable, and operable at scale.
Fleet / Orchestration: Turn raw capacity into reliable, efficient clusters and scheduling systems that researchers and product teams can use with minimal friction and great experience.
Core Network Engineering: Build and operate the high-performance networking fabrics, protocols, and observability needed for the largest training and serving workloads.
Hardware Health and Observability: Detect, diagnose, remediate, and prevent hardware and fleet-health issues so usable compute stays high across providers and accelerator generations.
Storage: Build scalable, performant, durable storage abstractions that keep data movement and storage access from becoming a bottleneck to research or products.
Agent Infrastructure: Build sandboxed execution infrastructure for agentic workloads across research and production, with strong isolation, reliability, and scale.
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
Have built or operated distributed systems, infrastructure platforms, high-performance computing environments, large-scale networking systems, Kubernetes clusters, developer tools, or production systems with demanding reliability requirements
Enjoy working across layers of the stack and are comfortable moving between software, hardware, networking, systems performance, reliability, and user needs
Care about making complex infrastructure understandable, observable, and usable for the people depending on it
Can diagnose hard problems under real operational pressure while still investing in long-term engineering quality
Like building leverage for others, whether through APIs, automation, debugging tools, CaaS and agent infrastructure primitives, workflow improvements, or better platform abstractions
Are motivated by scale, efficiency, reliability, and disciplined measurement through benchmarks, profiles, and production evidence
Communicate clearly, take ownership, and work well with teams whose constraints and goals differ from your own
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
Interest in working on infrastructure that directly enables frontier AI research and product impact
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
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