Senior Deep Learning Compiler Engineer, XLA

Nvidia

Confirmed live yesterday Trusted

Remote

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Work type: Remote
Location: Santa Clara, CAAustin, TXTXWACARedmond, WA
Employment: Full-time
Posted: 50 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1792 open roles on FindRole.

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At a glance

TL;DR · Senior Deep Learning Compiler Engineer, XLA

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The Senior Deep Learning Compiler Engineer - XLA will join the XLA team to develop compiler optimization algorithms for deep learning workloads. The role involves optimizing inference and training performance for the JAX framework and the OpenXLA compiler on NVIDIA GPUs at scale. Key responsibilities include crafting compiler optimization techniques for deep learning network graphs, designing novel graph partitioning and tensor sharding techniques for distributed training, and performing performance tuning. The engineer will perform code-generation for NVIDIA GPU backends using MLIR, LLVM, and OpenAI Triton. Required skills include C/C++ programming, performance analysis, and a strong foundation in CPU and GPU architectures. The work focuses on accelerating next-generation deep learning software by designing AI compiler features for hardware and developing user-facing features in JAX.

Skills

C++ XLA MLIR LLVM CUDA OpenAI Triton JAX PyTorch TensorFlow TVM OpenCL Distributed Programming High-Performance Computing Compiler Optimization Deep Learning Frameworks

What you'll do

Develop compiler optimization algorithms for deep learning workloads.
Optimize inference and training performance for the JAX framework and OpenXLA compiler.
Implement compiler optimization techniques for deep learning network graphs.
Design novel graph partitioning and tensor sharding techniques for distributed training and inference.
Perform performance tuning and analysis for GPU workloads.
Generate code for NVIDIA GPU backends using MLIR, LLVM, and OpenAI Triton.
Design user-facing features in JAX and related libraries.
Design AI compiler software features for next-generation GPUs with hardware engineering teams.

What we're looking for

Bachelor's, Master's, or Ph.D. in Computer Science, Computer Engineering, a related field, or equivalent experience.
4+ years of relevant work or research experience in performance analysis and compiler optimizations.
Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design.
Strong foundation in architecture of CPU, GPUs, or other high-performance hardware accelerators.
Knowledge of high-performance computing and distributed programming.
Ability to work independently, define project goals, and lead development efforts using clean software engineering and testing practices.
CUDA or OpenCL programming experience (preferred).
Experience with XLA, TVM, MLIR, LLVM, OpenAI Triton, deep learning models, and framework design (preferred).

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