Software Engineer, Inference – AMD GPU Enablement
About the Team Our Inference team brings OpenAI’s most capable research and technology to the world through our products. We empower consumers, enterprises and developers alike to use and access our state-of-the-art AI models, allowing them to do things that they’ve never been able to before.
What this role actually needs.
About the Team Our Inference team brings OpenAI’s most capable research and technology to the world through our products. We empower consumers, enterprises and developers alike to use and access our state-of-the-art AI models, allowing them to do things that they’ve never been able to before. Responsibilities: - 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. Company context: OpenAI builds frontier AI systems, research infrastructure, and applied products for developers, enterprises, and global users.
Day-to-day expectations
OpenAI lists these responsibilities for the Software Engineer, Inference – AMD GPU Enablement role.
- 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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Source: jobs.ashbyhq.com · Source ID: 9b79406c-89a8-49bd-8a38-e72db80996e9 · Confidence: 97/100 · Last checked: May 7, 2026
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