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# Software Engineer, Inference - AMD GPU Enablement

- **Company:** OpenAI Inc.
- **Location:** San Francisco, CA, United States
- **Salary:** $295,000.0
- **Contract:** Permanent contract
- **Skills:** Computer Clusters, Nvidia CUDA, Software Debugging, Graphics Processing Unit (GPU), Perf (Linux), Performance Monitor
- **Published:** September 5, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/85123067/1

## About the Role

* Have experience writing or porting GPU kernels using HIP, CUDA, or Triton, and care deeply about low-level performance.
 * Are familiar with communication libraries like NCCL/RCCL and understand their role in high-throughput model serving.
 * Have worked on distributed inference systems and are comfortable scaling models across fleets of accelerators.
 * Enjoy solving end-to-end performance challenges across hardware, system libraries, and orchestration layers.
 * Are excited to be part of a small, fast-moving team building new infrastructure from first principles.

Nice to Have:

 * Contributions to open-source libraries like RCCL, Triton, or vLLM.
 * Experience with GPU performance tools (Nsight, rocprof, perf) and memory/comms profiling.
 * Prior experience deploying inference on other non-NVIDIA GPU environments.
 * Knowledge of model/tensor parallelism, mixed precision, and serving 10B+ parameter models.

## Description

We're hiring engineers to scale and optimize OpenAI's inference infrastructure across emerging GPU platforms. You'll work across the stack - from low-level kernel performance to high-level distributed execution - and collaborate closely with research, infra, and performance teams to ensure our largest models run smoothly on new hardware.

This is a high-impact opportunity to shape OpenAI's multi-platform inference capabilities from the ground up with a particular focus on advancing inference performance on AMD accelerators.

In this role, you will:

 * Own bring-up, correctness and performance of the OpenAI inference stack on AMD hardware.
 * Integrate internal model-serving infrastructure (e.g., vLLM, Triton) into a variety of GPU-backed systems.
 * Debug and optimize distributed inference workloads across memory, network, and compute layers.
 * Validate correctness, performance, and scalability of model execution on large GPU clusters.
 * Collaborate with partner teams to design and optimize high-performance GPU kernels for accelerators using HIP, Triton, or other performance-focused frameworks.
 * Collaborate with partner teams to build, integrate and tune collective communication libraries (e.g., RCCL) used to parallelize model execution across many GPUs.


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