Senior AI Compiler Engineer - Applied Research
NVIDIA · California, United States
- Hybrid
- Full-time
- $241,500 / year
- California, United States
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Job highlights
- Develop AI technologies for compilation pipelines.
- Design AI solutions for GPU programming.
- Build training pipelines for ML models.
- Optimize performance using RL techniques.
- Collaborate on production toolchain integration.
About the role
About the Role
NVIDIA's GPUs are at the core of modern AI infrastructure, from training large-scale models to running inference in production. This 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
- Help trailblaze company efforts in applying AI within conventional compilation pipelines.
- Design and implement AI-based technology addressing core problems of low-level GPU programming.
- Build training pipelines for supervised fine-tuning and reinforcement learning (RL/RLHF-style or policy optimization variants).
- Define model inputs/outputs over compiler low level compiler representations.
- Develop evaluation frameworks to measure code quality, runtime, compile-time overhead, and correctness.
- Intelligent (domain` task based) prompt engineering.
- Collaborate with compiler engineers to integrate learned policies into production toolchains.
- Prototype and iterate on model architectures, prompts, and fine-tuning strategies for scheduling and allocation tasks.
- Create datasets from compiler traces, optimization passes, and target-specific performance signals.
- Apply RL techniques to optimize for downstream objectives (performance, spill reduction, instruction-level parallelism, etc.) and run rigorous experiments, ablations, and benchmarking across workloads and hardware targets.
What We Need To See
- M.S./PhD degree in Computer Engineering, Computer Science related technical field (or equivalent experience).
- 5+ years of experience building AI/ML systems.
- Strong software engineering skills in Python and at least one systems language (C++ preferred).
- Hands-on experience training/fine-tuning large models (Transformers, PEFT/LoRA, distributed training).
- Solid understanding of machine learning fundamentals and experimentation best practices.
- Experience with reinforcement learning (e.g., policy gradients, actor-critic, offline RL, bandit-style optimization).
- Knowledge of prompt-engineering techniques.
- Ability to work across research and engineering, from prototype to production.
Ways To Stand Out From The Crowd
- Distributed training/inference at scale.
- Experience working with the NVIDIA NeMo framework.
- Understanding of GPU performance, experience with benchmarking suites and performance profiling tools.
- Formal methods or static analysis familiarity for correctness guarantees.
- CUDA programming experience.
Key skills/competency
- AI Compiler Engineering
- Applied Research
- Machine Learning
- Low-level Optimization
- GPU Programming
- Reinforcement Learning
- Python
- C++
- Distributed Training
- Prompt Engineering
Skills & topics
- AI Compiler Engineer
- Applied Research
- Machine Learning
- Low-level Optimization
- GPU Programming
- Reinforcement Learning
- Python
- C++
- Distributed Training
- Prompt Engineering
- Computer Science
- Computer Engineering
- AI Systems
- Transformers
- PEFT
- LoRA
- NVIDIA NeMo
- CUDA
How to get hired
- Tailor your resume: Highlight your AI/ML systems experience, Python/C++ skills, and any experience with large model training or reinforcement learning.
- Showcase relevant projects: Emphasize your contributions to compiler technologies, GPU programming, or distributed systems.
- Prepare for technical interviews: Be ready to discuss low-level GPU programming, AI model training, and reinforcement learning concepts.
- Demonstrate research and engineering skills: Highlight your ability to move from prototype to production.
Technical preparation
Master AI/ML fundamentals and experimentation.,Practice Python and C++ coding.,Study reinforcement learning techniques.,Review GPU architecture and CUDA.
Behavioral questions
Describe a complex AI system you built.,How do you handle research/engineering challenges?,Explain your experience with distributed systems.,How do you ensure code quality and correctness?
Frequently asked questions
- What specific AI/ML systems experience is NVIDIA looking for in a Senior AI Compiler Engineer?
- NVIDIA seeks at least 5 years of experience building AI/ML systems. This includes hands-on experience training and fine-tuning large models like Transformers, utilizing techniques such as PEFT/LoRA and distributed training. A solid understanding of machine learning fundamentals and experimentation best practices is also crucial for this Senior AI Compiler Engineer role.
- What programming languages are essential for the Senior AI Compiler Engineer position at NVIDIA?
- Strong software engineering skills in Python are required. Additionally, proficiency in at least one systems language, with a preference for C++, is essential for this Senior AI Compiler Engineer role. This combination ensures candidates can handle both high-level AI development and low-level system optimizations.
- How does NVIDIA leverage AI in its hiring process for the Senior AI Compiler Engineer role?
- NVIDIA utilizes AI tools in its recruiting processes. This means that AI may be used to screen applications, identify suitable candidates, or assist in other aspects of the hiring workflow for roles like the Senior AI Compiler Engineer. Candidates should ensure their resumes are well-structured and keyword-optimized.
- What is the application deadline for the Senior AI Compiler Engineer position?
- Applications for this Senior AI Compiler Engineer position will be accepted at least until June 15, 2026. It's advisable to apply well before this date to ensure your application is considered thoroughly.
- What kind of experience helps a candidate stand out for the Senior AI Compiler Engineer role at NVIDIA?
- To stand out for the Senior AI Compiler Engineer position, candidates can highlight experience in distributed training/inference at scale, familiarity with the NVIDIA NeMo framework, understanding of GPU performance and profiling, experience with formal methods or static analysis, and CUDA programming experience. These advanced skills demonstrate a deeper alignment with NVIDIA's core technologies.