Senior Deep Learning Compiler Engineer – XLA

NVIDIA

Deep learning compiler engineer optimizing JAX and OpenXLA for NVIDIA GPUs. Building high-performance AI compiler software with framework and hardware teams.

Posted 8/14/2026full-timeRemote • California, Texas, Washington • 🇺🇸 United StatesSenior💰 $152,000 - $287,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing compiler optimization algorithms for deep learning workloads, with a strong focus on performance tuning and analysis for NVIDIA GPUs. Proficient in software engineering practices, mentoring, and collaboration with cross-functional teams.

Highest-signal resume keywords

Compiler Optimization AlgorithmsPerformance AnalysisC/C++ ProgrammingDeep Learning FrameworksNVIDIA GPU Programming

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Applicant Tracking System Keywords

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Hard Skills

Compiler OptimizationPerformance TuningSoftware EngineeringDebuggingTest DesignGraph PartitioningTensor ShardingCode GenerationDistributed ProgrammingDeep Learning Algorithms

Soft Skills

Interpersonal SkillsTeam CollaborationMentoring

Tools & Technologies

JAXOpenXLAMLIRLLVMOpenAI TritonCUDAOpenCLXLATVMPyTorch

Industry Keywords

High-Performance ComputingDeep LearningGPU ArchitectureAI Compiler Software

Tech Stack

Tools & technologies

PyTorchTensorflow

About the role

Key responsibilities & impact

Develop compiler optimization algorithms for deep learning workloads
Optimize inference and training performance for the JAX framework and OpenXLA compiler on NVIDIA GPUs at scale
Collaborate with deep learning framework partners and hardware architecture teams
Craft and implement compiler optimization techniques for deep learning network graphs
Design graph partitioning and tensor sharding techniques for distributed training and inference
Perform performance tuning and analysis
Implement code generation for NVIDIA GPU backends using MLIR, LLVM, and OpenAI Triton
Design user-facing features in JAX and related libraries
Perform general software engineering work
Work with GPU hardware engineering teams to design AI compiler software features for next-generation GPUs
Define project goals and scope and lead development efforts
Apply software engineering and testing practices
Mentor junior engineers and interns

Requirements

What you’ll need

Bachelor's, Master's, or Ph.D. in Computer Science, Computer Engineering, related field, or equivalent experience
4+ years of relevant work or research experience in performance analysis and compiler optimizations
Ability to work independently, define project goals and scope, and lead development efforts
Clean software engineering and testing practices
Excellent C/C++ programming and software design skills
Experience with debugging, performance analysis, and test design
Strong foundation in CPU, GPU, or other high-performance hardware accelerator architecture
Knowledge of high-performance computing and distributed programming
CUDA or OpenCL programming experience desired but not required
Experience with XLA, TVM, MLIR, LLVM, OpenAI Triton, deep learning models and algorithms, or deep learning framework design is a plus
Strong interpersonal skills and ability to work in a dynamic product-oriented team
Experience with JAX, PyTorch, or TensorFlow; CUDA or GPUs; and open-source compilers such as XLA, LLVM, MLIR, or TVM are ways to stand out

Benefits

Comp & perks

Competitive salaries
Generous benefits package
Equity
Benefits