软件工程师，工作负载赋能
Software Engineer, Workload Enablement
关于团队
Scaling 团队负责 OpenAI 基础设施的架构和工程核心。我们设计并交付先进的系统，以支持尖端 AI 模型的部署和运行。我们的工作涵盖系统软件、网络、平台架构、集群级监控以及性能优化。
关于职位
我们正在招聘一名 SW 工程师，以在新平台上实现生产负载和端到端测试。该职位将包括创建新的测试框架和平台压力基准，将现有的推理和训练负载迁移到新的、有时是早期访问的系统/硬件，分析性能和瓶颈，并描述新系统的端到端行为（计算、通信、存储、控制平面和故障模式）。
主要职责
- 在新平台/SKU 上迁移并验证关键推理和训练负载；推动其达到内部就绪标准，确保正确性、性能和稳定性。
- 构建一套基准测试和压力测试，通过全面测试系统的所有方面（包括 CPU、GPU、内存子系统、前端、扩展上和扩展外网络（包括 WAN 流量、NVLink 和 RDMA 集体操作）、存储、热力等），来捕捉我们负载的真实端到端行为。
- 深入进行分布式训练/推理性能分析：
- 集体性能和调优（跨 NCCL/RCCL 和内部库）
- 计算/通信重叠、内核级瓶颈、内存带宽和调度影响
- 创建可在 CI/实验室环境中运行的可重复测试框架，并生成可操作的输出（通过/失败、性能分数、回归检测）。
- 与系统和集群启动工程师合作，确保平台不仅稳定且高性能，而且具备可操作性和可扩展性（容器化、K8s 集成、遥测钩子、故障排查循环）。
- 跨职能与供应商和内部利益相关者合作，通过生成清晰的错误报告、最小复现步骤和优先级问题列表来推进问题解决。
资格要求
- 计算机科学或电子工程学士学位（或同等实践经验）。
- 5 年以上在以下任一领域的工作经验：ML 系统、性能工程、分布式系统或 HPC。
- 具有以下方面的实际经验：
- PyTorch 和现代 LLM 训练/推理堆栈
- 大规模分布式训练概念（数据/模型/流水线并行，集体通信）
- 有 R 的使用经验
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About the Team
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.
About the Role
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).
Key Responsibilities
- 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.
Qualifications
- 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).
Preferred Skills
- 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).
About OpenAI
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.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf.
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