OpenAI의 Scaling 팀에서 차세대 AI 모델을 위한 인프라를 구축할 소프트웨어 엔지니어를 채용합니다. ML 시스템, 분산 학습, 성능 엔지니어링 분야에서 5년 이상의 경력이 필수입니다. 새로운 하드웨어 플랫폼에서 워크로드를 최적화하고, 대규모 분산 시스템의 성능을 분석 및 검증하는 핵심 역할을 수행하게 됩니다. PyTorch, NCCL/RCCL, RDMA 기술 스택에 능숙한 분들의 많은 지원 바랍니다.
The Scaling team is responsible for the architectural and engineering backbone of OpenAI’s infrastructure. We design and deliver advanced systems that support the deployment and operation of cutting-edge AI models. Our work spans system software, networking, platform architecture, fleet-level monitoring, and performance optimization.
We’re hiring an SW Engineer to enable production workloads and end-to-end testing on new platforms. This role will include creating new test harnesses and platform stress benchmarks, porting existing inference and training workloads to new, sometimes early-access, systems/hardware, analyzing performance and bottlenecks, and characterizing the end-to-end behavior of new systems (compute, comms, storage, control plane, and failure modes).
Port and validate key inference and training workloads on new platforms/SKUs as they arrive; drive correctness, performance, and stability to an internal readiness bar.
Build a suite of benchmarks and stress tests that capture real E2E behavior of our workloads by exercising all aspects of a system, including CPU, GPU, memory subsystem, frontend, scale-up, and scale-out networking (including WAN traffic, NVlink and RDMA collectives), storage, thermals, and any other relevant parts.
Deep-dive performance on distributed training/inference:
Collective performance and tuning (across NCCL/RCCL and internal libraries)
Overlap of compute/communication, kernel-level bottlenecks, memory bandwidth and scheduling effects
Create repeatable test harnesses that run in CI / lab environments and produce actionable outputs (pass/fail, performance score, regression detection).
Partner with systems + fleet bring-up engineers to ensure the platform is not only stable and performant, but also operationally usable and scalable (containerization, K8s integration, telemetry hooks, failure triage loops).
Work cross-functionally with vendors and internal stakeholders by producing clear bug reports, minimal repros, and prioritized issue lists.
BS in CS/EE (or equivalent practical experience).
5+ years in one or more of: ML systems, performance engineering, distributed systems, or HPC.
Strong hands-on experience with:
PyTorch and modern LLM training/inference stacks
Large-scale distributed training concepts (data/model/pipeline parallel, collective comms)
Experience with RDMA and debugging/optimizing comms libraries (NCCL or RCCL) and their interaction with hardware/network
Proficiency in Python plus comfort reading/writing performance-critical code (C++/CUDA/HIP is a plus).
Strong profiling/debugging skills (e.g., Nsight, rocprof, perf, flamegraphs; ability to reason from traces/counters).
Experience building workload-shaped benchmarks and stress/fault tests that correlate to production behavior (not just synthetic loops or microbenchmarks).
Familiarity with RDMA networking and transport tuning; understanding of how network topology and congestion impact collectives.
Experience running and validating workloads in Kubernetes, and bridging “research code” into robust, repeatable infrastructure.
Hands-on lab experience with early hardware (new NICs, new GPUs/accelerators, early racks).
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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