Hardware / Software CoDesign Engineer

Co-designs AI-optimized hardware with vendors, develops performance models, and influences architectures for efficient ML training and inference. Requires 4+ years experience in software/hardware co-design, GPU programming, and collaboration with ML/kernel teams.

About the job

In this role, you will:

Co-design future hardware for programmability and performance with our hardware vendors
Assist hardware vendors in developing optimal kernels and add support for it in our compiler
Develop performance estimates for critical kernels for different hardware configurations and drive decisions on compute core and memory hierarchy features
Build system performance models at different abstraction levels and carry out analysis to drive decisions on scale up, scale out, front end networking
Work with machine learning engineers, kernel engineers and compiler developers to understand their vision and needs from high performance accelerators
Manage communication and coordination with internal and external partners
Influence the roadmap of hardware partners to optimize them for OpenAI’s workloads
Evaluate potential partners’ accelerators and platforms
As the scope of the role and team grows, understand and influence roadmaps for hardware partners for our datacenter networks, racks, and buildings

You might thrive in this role if you have:

4+ years of industry experience, including experience harnessing compute at scale and optimizing ML platform code to run efficiently on target hardware
Strong experience in software/hardware co-design
Deep understanding of GPU and/or other AI accelerators
Experience with CUDA, Triton or a related accelerator programming language
Experience driving Machine Learning accuracy with low precision formats
Experience with system performance modeling and analysis to optimize ML model deployment
Strong coding skills in C/C++ and Python
Are familiar with the fundamentals of deep learning computing and chip architecture/microarchitecture
Able to actively collaborate with ML engineers, kernel writers, compiler developers, system engineers, chip architects/microarchitects

Nice to have:

PhD in Computer Science and Engineering with a specialization in Computer Architecture, Parallel Computing, Compilers or other Systems
Strong understanding of LLMs and challenges related to their training and inference