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About the Role
Drive hardware/software co-design for AI silicon architecture, connecting model and workload requirements with compute, memory, and acceleration capabilities to maximize performance, efficiency, and scalability. Partner closely with ML research, compiler, and silicon architecture teams to jointly develop software approaches and hardware capabilities, evaluating tradeoffs to improve performance, efficiency, and utilization.
Responsibilities
- Drive hardware/software co-design across ML research, compiler, and silicon teams, jointly shaping kernel implementations and hardware capabilities to meet workload performance and efficiency goals.
- Partner with ML research to translate model and workload needs into kernel performance targets and compute, memory capacity, and bandwidth requirements, feeding hardware constraints and opportunities back into model and algorithm choices.
- Map and optimize ML kernels for architecture, using insights from data movement, memory hierarchy, and execution efficiency to guide both software improvements and hardware design.
- Co-design support for new data structures and quantization formats across kernels, compilers, and silicon, balancing model quality, numerical behavior, performance, power, and implementation cost.
- Define and help develop specialized acceleration IP, working with compiler and silicon partners to determine the right division of functionality across software, instructions, and dedicated hardware.
- Drive ML performance modeling and simulation with the compiler team to compare hardware/software design options, expose bottlenecks, and quantify end-to-end benefits on representative workloads.
- Partner with the Silicon Architecture lead and silicon team to specify and validate new IP, using kernel and simulation results to refine requirements and prioritize architectural improvements.
Requirements
- Deep experience with ML accelerator architecture and how compute resources, memory systems, and data movement shape workload performance.
- Ability to translate model-level behavior into concrete kernel requirements and hardware design decisions.
- Experience building or using accelerator performance models and simulators, including validating assumptions and interpreting limitations.
- Hands-on experience optimizing ML kernels, including identifying bottlenecks and improving utilization, locality, and execution efficiency.
- Understanding of quantization and numerical formats, including their implications for model quality, memory bandwidth, compute efficiency, and hardware support.
- Experience with hardware/software co-design and reasoning about capability placement across kernels, compilers, instruction sets, or dedicated hardware.
- Ability to work effectively across research, compiler, and silicon teams, bringing clarity to ambiguous requirements and building alignment through technical evidence.
- May need to meet certain legal status requirements to comply with U.S. export control laws and regulations.
Skills
- ML accelerator architecture
- Performance modeling
- Simulation
- ML kernel optimization
- Quantization
- Numerical formats
- Hardware/software co-design
Location
- Remote
- Hybrid
- Onsite
Work Type
- Full-time
Experience Level
- Lead
About the Company
- 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 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.
Equal Opportunity
- 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.
- 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.
- We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.