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. We focus on performant and efficient model inference, as well as accelerating research progression via model inference.
About the Role
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.
You can thrive in this role if you:
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.
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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