Compiler Engineer - Deep Learning

Requirements:

• 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 technologies like XLA, TVM, MLIR, LLVM, OpenAI Triton, deep learning models and algorithms, and deep learning framework design.
• Strong interpersonal skills and ability to work in a dynamic product-oriented team. A history of mentoring junior engineers and interns is a bonus.

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

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

Perks and benefits:

• Highly competitive salaries
• Comprehensive benefits package
• Equity opportunities