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NVIDIA

Designs the chips and software that power AI

Senior Compiler Engineer - AI

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote

About the Job

Requirements

  • A BS, MS, or PhD in Computer Science or a related field, or equivalent experience, with a focus on machine learning and compiler/developer tools.
  • At least 8 years of software engineering and AI/ML experience, preferably in tools or systems development.
  • Strong knowledge of compilers, code generation, and GPU architecture.
  • Demonstrated proficiency in Python and C/C++.
  • A strong mathematical and scientific foundation relevant to AI/ML and compiler technologies.
  • Hands-on experience with LLVM/MLIR-based compiler development, including optimization passes, code generation, or frontend integration.
  • Experience building or integrating LLM-based systems, agents, or developer tooling in production environments.

Responsibilities

  • Join a team driving solutions in compilers and developer tools through applied machine learning and AI.
  • Collaborate with engineers to shape compiler capabilities that power large-scale products.
  • Design and implement end-to-end compiler optimization workflows, from feature engineering and model development to compiler integration and production rollout.
  • Develop and integrate learning-based decision systems into compiler passes, code generation flows, and optimization pipelines.
  • Build interfaces between LLM systems and compiler infrastructure for optimization recommendations, decision support, and workflow automation.
  • Partner with compiler, architecture, and performance teams to validate impact on representative workloads and benchmark suites.
  • Lead experimentation, evaluation, and deployment with standards for reliability, scalability, and performance.

Desired Qualifications

  • Deep familiarity with reinforcement learning, genetic/evolutionary algorithms, predictive modeling, and complex systems.
  • A proven track record of deploying AI/ML solutions in production and embedded environments.
  • Strong experience integrating LLMs into systems workflows with attention to reliability, latency, and evaluation quality.
  • Demonstrated measurable outcomes such as runtime gains, compile-time improvements, or efficiency improvements on real-world workloads.
  • Publications, open-source contributions, or patents in compilers, AI/ML systems, or performance engineering.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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