Senior AI Compiler Engineer - Applied Research

About the role

NVIDIA is seeking an AI Research Engineer / Applied Scientist with expertise in compilers and low-level optimization to contribute to the development of groundbreaking technologies in machine learning compilers and AI systems. The ideal candidate will 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, and prototype and iterate on model architectures, prompts, and training strategies for NP-hard problems in optimizing compilers.

Responsibilities

• 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.

Requirements

• M.S. or PhD degree in Computer Engineering, Computer Science related technical field (or equivalent experience).
• 5+ years of experience building AI/ML systems.
• Strong 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.

Qualifications

• 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.

Benefits

Competitive salaries and a generous benefits package are offered. NVIDIA is widely considered to be one of the technology world’s most desirable employers. With unprecedented growth, our exclusive engineering teams are rapidly growing. 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.