Senior AI Compiler Engineer - Applied Research

NVIDIA's GPUs are at the core of modern AI infrastructure, from training large-scale models to running inference in production. That position depends on software as much as hardware, and compiler engineering is a big part of what makes it work.We are looking for outstanding AI Research Engineer /Applied Scientist focused on Compilers /Low-level optimization to join the team and develop groundbreaking technologies in machine learning compilers and AI systems. We build innovative AI compiler solutions that work together with NVIDIA's software stack to provide comprehensive acceleration for modern machine learning models.What you'll be doing:Design and implement AI-based technology addressing core problems of low-level GPU code generation.Build SFT and RL training pipelines.Define model inputs using low-level compiler representations.Define, implement, and evaluate strategies for intelligent prompt engineering in compilation domain.Prototype and iterate on model architectures, prompts, and training strategies for NP-hard problems in optimizing compilers.Prepare datasets from compiler traces, optimization passes, and target-specific performance signals.Apply RL techniques to optimize for downstream objectives and run rigorous experiments, analysis, and benchmarking across workloads and hardware targets.Build rigorous benchmarks to assess code quality, correctness, and generation overhead.Partner with compiler engineers to integrate and ship learned policies with production toolchains.What we need to see:M.S. or PhD degree in Computer Engineering, Computer Science related technical field (or equivalent experience).5+ years of experience building AI/ML systems.Solid understanding of machine learning fundamentals and experimentation best practices.Strong software engineering skills in Python and C++.Hands-on experience training/fine-tuning/post-training large models.Experience with reinforcement learning.Reward modeling from non-differentiable signals (binary runtime/compile success, performance counters).Knowledge of prompt-engineering techniques (CoT, chaining/orchestration, context adaptation, etc).Ability to work across research and engineering, from prototype to production.CUDA programming experience and GPU performance familiarity.Ways to stand out from the crowd:Distributed training/inference at scale (Megatron, NeMo, vLLM, Triton).Experience working with the NVIDIA training stacks.Fundamentals of construction of optimizing compilers.Understanding of GPU performance, experience with benchmarking suites and performance profiling tools.Knowledge of formal methods or static analysis for correctness guarantees.With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous program manager with a real passion for technology, we want to hear from you.Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD.You will also be eligible for equity and benefits.Applications for this job will be accepted at least until July 28, 2026.This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.SummaryLocation: US, CA, Santa Clara; US, TX, Austin; US, TX, Remote; US, WA, Remote; US, CA, Remote; US, WA, RedmondType: Full time