Senior AI Compiler Engineer – Applied Research

🕒 June 16

🏄 California, Texas, +1 more states – Remote

🏄 California – Remote 🤠 Texas – Remote ☕ Washington – Remote

💵 $152k - $241.5k / year

⏰ Full Time

🟠 Senior

🤖 Artificial Intelligence

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• 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. • 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.

🏖️ Benefits

• equity • benefits