About the Team
Our team brings OpenAI's most capable research and technology to the world through our products. We empower consumers, enterprises, and developers alike to access state-of-the-art AI models - unlocking new capabilities across productivity, creativity, and more. We focus on high-performance model inference and accelerating research through efficient and reliable infrastructure.
About the Role
We're looking for a hands-on Tech Lead to drive the design, optimization, and scaling of our inference systems. In this role, you'll lead engineering efforts to ensure our largest models run with exceptional efficiency in high-throughput, low-latency environments. You'll be responsible for shaping our CUDA strategy, driving performance at the kernel level, and collaborating across teams to deliver end-to-end production readiness.
In this role, you will:
Lead the design and implementation of core inference infrastructure for serving frontier AI models in production.
Own and optimize CUDA-based systems and kernels to maximize performance across our fleet.
Partner with researchers to integrate novel model architectures into performant, scalable inference pipelines.
Build tooling and observability to detect bottlenecks, guide system tuning, and ensure stable deployment at scale.
Collaborate cross-functionally to align technical direction across research, infra, and product teams.
Mentor engineers on GPU performance, CUDA development, and distributed inference best practices.
You may thrive in this role if you:
Have deep expertise in CUDA, including writing and optimizing high-performance kernels for inference or training workloads.
Have experience leading complex engineering efforts, particularly at the systems and performance layer of large-scale ML infrastructure.
Understand the full inference stack - from model loading and memory management to communication libraries and deployment orchestration.
Are comfortable working in large, distributed GPU environments and debugging performance issues across hardware and software layers.
Have strong familiarity with PyTorch and NVIDIA's GPU software stack (NCCL, NVLink, MIG, etc.).
Take a systems-level view, but aren't afraid to dive into low-level code when performance is on the line.
Bonus:
Experience with inference frameworks like TensorRT, vLLM, SGLang, or custom model parallelism infrastructure.
Familiarity with TPU, AMD GPUs, ROCm, HIP, TensorRT-LLM, Ray Serve, Megatron, MPI, or Horovod.
Familiarity with profiling tools (Nsight, nvprof, or custom observability stacks).
Background in HPC or large-scale distributed systems engineering.
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 do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.
OpenAI Affirmative Action and Equal Employment Opportunity Policy Statement
For US Based Candidates: Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records.
We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.
OpenAI Global Applicant Privacy Policy
At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
Compensation Range: $460K - $685K
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