Senior AI Compiler Engineer - Applied Research

Nvidia

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CAAustin, TX
Salary: $152,000–$241,500 / yr
Posted: 4 days ago
Nearby: 99+ roles within 25 mi

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Competitive pay

How this pay compares to similar roles

Similar $202k

This role $197k

$141k most similar roles pay here $252k

This role pays less than 52% of similar roles. Most pay $166,999–$237,625 — the shaded band above. At the midpoint, this role pays about $197k versus about $202k for comparable roles.

Based on 240 similar postings.

Employer

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 994 open roles on FindRole.

Listed pay typically runs $168,000–$270,250 across 977 roles with salary data.

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At a glance

TL;DR · Senior AI Compiler Engineer - Applied Research

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Join NVIDIA’s cutting-edge AI Research team as an Applied Scientist specializing in Compilers/Low-level Optimization to develop innovative machine learning compilers that integrate seamlessly with NVIDIA's software stack. Your role involves designing and implementing AI-based technologies to address core challenges in low-level GPU programming, building training pipelines for supervised fine-tuning and reinforcement learning, and creating evaluation frameworks to measure code quality and performance. You will collaborate closely with compiler engineers to integrate learned policies into production toolchains, prototype model architectures, and create datasets from compiler traces. Essential skills include a M.S./PhD in Computer Engineering or related field, 5+ years of AI/ML system development experience, proficiency in Python and C++, hands-on training of large models, and expertise in reinforcement learning techniques. Knowledge of the NVIDIA NeMo framework and CUDA programming is highly desirable.

Skills

Python C++ CUDA Reinforcement_Learning Prompt_Engineering NVIDIA_NeMo Distributed_Training GPU_Performance_Benchmarking Static_Analysis Transformer_Models PEFT LoRA Machine_Learning_Experimentation CI/CD

What you'll do

Design and implement AI-based technologies to solve low-level GPU programming challenges.
Build training pipelines for supervised fine-tuning and reinforcement learning models.
Develop evaluation frameworks to measure code quality and runtime performance.
Create datasets from compiler traces and optimization passes for analysis.
Apply RL techniques to optimize downstream objectives in various workloads.

What we're looking for

M.S./PhD in Computer Engineering or related field with 5+ years of AI/ML system development.
Expertise in Python and C++ for software engineering and systems programming.
Hands-on experience training and fine-tuning large models like Transformers.
Strong understanding of machine learning fundamentals and reinforcement learning techniques.
Knowledge of prompt-engineering, GPU performance optimization, and benchmarking tools.
Experience integrating learned policies into production toolchains using NVIDIA NeMo framework.

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