Nvidia Compiler Engineer - Deep Learning 

United States, Texas 

840503542

As a member of the Deep Learning Compiler Team, you will be responsible for developing compiler optimization algorithms for deep learning networks. You will be driving inference and training performance of JAX framework and XLA and OpenXLA compilers on NVIDIA GPUs at scale. You’ll collaborate with our partners in deep learning framework teams and our hardware architecture teams to accelerate the next generation of deep learning software.

What you'll be doing:

• Crafting and implementing compiler optimization techniques for deep learning network graphs

• Designing novel graph partitioning and tensor sharding techniques for distributed training and inference

• Performance tuning and analysis

• Code-generation for NVIDIA GPU backends using open-source compilers such as MLIR, LLVM and OpenAI Triton.

• Defining APIs in JAX and related libraries and other general software engineering work

What we need to see:

• Bachelors, Masters or Ph.D. in Computer Science, Computer Engineering, related field (or equivalent experience)

• 2+ years of relevant work or research experience in performance analysis and compiler optimizations.

• Ability to work independently, define project goals and scope, and lead your own development effort adopting clean software engineering and testing practices.

• Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design.

• Strong foundation in CPU and/or GPU architecture. Knowledge of high-performance computing and distributed programming. CUDA or OpenCL programming experience is desired but not required.

• Experience with the following technologies is a huge plus: XLA, TVM, MLIR, LLVM, OpenAI Triton, deep learning models and algorithms, and deep learning framework design.

• Strong interpersonal skills are required along with the ability to work in a dynamic product-oriented team. A history of mentoring junior engineers and interns is a bonus.

Ways to stand out from the crowd:

• Worked on a deep learning framework such as JAX, Pytorch or Tensorflow.

• Experience with CUDA or with GPUs

• Proficient with open-source compilers such as LLVM and MLIR.