OpenAI· Scaling· San Francisco and Seattle
Software Engineer, Workload Enablement
Comp$293K – $455K
Classified Tasks (15)
Automate 0%Augment 93%Human-Only 7%
Augment (14)
AI assists, human decides
Enable production workloads and end-to-end testing on new platforms
operational
Create new test harnesses and platform stress benchmarks that capture real end-to-end workload behavior
technical
Port inference and training workloads to new or early-access systems and hardware
technical
Validate ported workloads for correctness, performance, and stability against internal readiness standards
analytical
Analyze performance metrics and identify bottlenecks across compute, communication, storage, and control plane
analytical
Characterize end-to-end system behavior including compute, communications, storage, control plane, and failure modes
analytical
Build a suite of benchmarks and stress tests that exercise CPU, GPU, memory subsystem, frontend, scale-up and scale-out networking (WAN, NVLink, RDMA), storage, and thermals
technical
Perform deep-dive performance analysis on distributed training and inference workloads
analytical
Analyze and tune collective communication performance across NCCL, RCCL, and internal libraries
technical
Investigate and optimize compute/communication overlap, kernel-level bottlenecks, memory bandwidth, and scheduling effects
technical
Create repeatable CI and lab test harnesses that produce actionable outputs (pass/fail, performance scores, regression detection)
operational
Integrate workloads with containerization and Kubernetes deployments to support operational usability and scalability
technical
Implement telemetry instrumentation and failure triage processes for platform bring-up and operations
operational
Produce clear bug reports, develop minimal reproducers, and maintain prioritized issue lists for vendors and internal stakeholders
communication
Human-Only (1)
Requires human judgment
Partner with systems and fleet bring-up engineers and vendors to triage and resolve platform issues
communication
Job description
Software Engineer, Workload Enablement | OpenAI Careers ## Software Engineer, Workload Enablement Scaling - San Francisco and Seattle Apply now(opens in a new window) **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