Do you want to help drive the development of high-performance, power-efficient datacenter solutions for Deep Learning? Do you have an interest in how system architecture across GPU, networking, CPU and IO relate to brand new generative AI capabilities? Come join our team, and bring your experience and interests to help us optimize our next generation of inference and training frameworks/frameworks and to redefine the deep learning industry once again.
As a Machine Learning Systems Engineer, you will
- Communicate with our product teams and profile ML/DL workloads to acquire an in-depth understanding of the problems.
- Design and implement novel solutions to solve the problems.
- Survey and possibly reproduce the state-of-the-art research work; analyze and evaluate if the ideas from the research work could be applied to our solutions.
- Write unit tests and benchmarks to validate and evaluate our solutions.
You may be a good fit, if you have:
- 2+ years of experience in researching or contributing to ML/DL systems and frameworks (including the time of being a graduate student).
- Excellent communication skills and the ability to work in a team.
- Strong coding skills (in at least one of Python and C++).
- Solid fundamentals in machine learning and deep learning topics.
- Solid fundamentals in other computer science and computer engineering topics: algorithms and data structures, operating systems, computer architecture, etc.
- Experience with GPU architecture and programming: CUDA and its related libraries and toolkits (e.g., cuDNN, cuBLAS, CUTLASS, nvprof, Nsight Compute, Nsight Systems, etc.); ROCm and its related libraries and toolkits.
- Experience with TPU.
- Strong academic records for candidates with bachelor’s degrees. Strong publication records in top ML/DL or computer system and architecture venues for candidates with master or PhD degrees.
