[Remote] Senior AI Compiler Engineer - Applied Research

Note: The job is a remote job and is open to candidates in USA. NVIDIA is a leader in AI infrastructure, focusing on developing cutting-edge technologies in machine learning compilers and AI systems. The Senior AI Compiler Engineer will design and implement AI-based technologies for low-level GPU code generation and collaborate with compiler engineers to enhance production toolchains.

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

Skills

• 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
• 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

• You will also be eligible for equity and [benefits](https://www.nvidia.com/en-us/benefits/).

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