Why This Job is Featured on The SaaS Jobs
Workload enablement sits at an increasingly important intersection in SaaS: the reliability of customer-facing AI features depends on infrastructure that can validate new compute platforms before they become the default. This role is notable because it focuses on bringing real training and inference workloads onto early-access hardware, then proving readiness through end-to-end characterization across compute, networking, storage, and failure modes.
For a long-term SaaS engineering path, the work builds durable skills in performance engineering and operability, where improvements translate into predictable service behavior at scale. Creating benchmark suites, CI-ready harnesses, and regression signals is the kind of systems craftsmanship that carries across SaaS environments, especially as more products embed ML and require disciplined capacity planning, observability, and incident-prevention through testing.
The position fits engineers who prefer concrete signals over abstractions: traces, counters, reproducible repros, and measurable bottlenecks. It also suits those comfortable collaborating across infrastructure, fleet bring-up, and external vendors, where progress depends on clear technical writing and prioritization. Candidates interested in the pragmatic edge between research workloads and production platforms will find the scope aligned with modern AI-driven SaaS delivery.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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