OpenAI의 Hardware 조직에서 모델 연구와 실리콘 아키텍처를 연결하는 ML Research Engineer를 채용합니다. Python, C++, CUDA 또는 Triton 활용 능력이 필수이며, Transformer 모델과 양자화(Quantization)에 대한 깊은 이해가 필요합니다. 하드웨어와 소프트웨어의 경계에서 성능 최적화와 시스템 설계를 주도할 인재를 찾습니다.
OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform.
We’re seeking a Research-Hardware Codesign Engineer to operate at the boundary between model research and silicon/system architecture. You’ll help shape the numerics, architecture, and technology bets of future OpenAI silicon in collaboration with both Research and Hardware.
Your work will include debugging gaps between rooflines and reality, writing quantization kernels, derisking numerics via model evals, quantifying system architecture tradeoffs, and implementing novel numeric RTL. This is a hands-on role for people who go looking for hard problems, get to ground truth, and drive it to production. Strong prioritization and clear, honest communication are essential.
Location: San Francisco, CA (Hybrid: 3 days/week onsite)
Relocation assistance available.
Build on our roofline simulator to track evolving workloads, and deliver analyses that quantify the impact of system architecture decisions and support technology pathfinding.
Debug gaps between performance simulation and real measurements; clearly communicate root cause, bottlenecks, and invalid assumptions.
Write emulation kernels for low-precision numerics and lossy compression schemes, and get Research the information they need to trade efficiency with model quality.
Prototype numerics modules by pushing RTL through synthesis; hand off novel numerics cleanly, or occasionally own an RTL module end-to-end.
Proactively pull in new ML workloads, prototype them with rooflines and/or functional simulation, and drive initial evaluation of new opportunities or risks.
Understand the whole picture from ML science to hardware optimization, and slice this end-to-end objective into near-term deliverables.
Build ad-hoc collaborations across teams with very different goals and areas of expertise, and keep progress unblocked.
Communicate design tradeoffs clearly with explicit assumptions and confidence levels; produce a trail of evidence that enables confident execution.
An exceptional track record of high-quality technical output, and a bias for shipping a prototype now and iterating later in the absence of clear requirements.
Strong Python, and C++ or Rust, with a cautious attitude toward correctness and an intuition for clean extensibility.
Experience writing Triton, CUDA, or similar, and an understanding of the resulting mapping of tensor ops to functional units.
Working knowledge of PyTorch or JAX; experience in large ML codebases is a plus.
Practical understanding of floating point numerics, the ML tradeoffs of reduced precision, and the current state of the art in model quantization.
Deep understanding of transformer models, and strong intuition for transformer rooflines and the tradeoffs of sharded training and inference in large-scale ML systems.
Experience writing RTL (especially for floating point logic) and understanding of PPA tradeoffs is a plus.
Strong cross-functional communication (e.g. across ML researchers and hardware engineers); ability to slice ambiguous early-incubation ideas into concrete arenas in which progress can be made.
To comply with U.S. export control laws and regulations, candidates for this role may need to meet certain legal status requirements as provided in those laws and regulations.
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.
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