Hybrid Senior Machine Learning Applications, Compiler Engineer

at NVIDIA

• Job title: Senior Machine Learning Applications, Compiler Engineer
• Job type: Full Time
• Experience level: Senior
• Salary: $152,000 - $287,500 per year
• Degree requirement: Postgraduate Degree
• Tech skills: PyTorch, Rust, Tensorflow
• Location requirements: Hybrid, Santa Clara, United States

About the role

• Senior Engineer developing innovative algorithms and optimizations for NVIDIA’s inference and compiler stack. Collaborating across systems, compilers, and deep learning workloads.

Responsibilities

• Build, develop, and maintain high-performance runtime and compiler components, focusing on end-to-end inference optimization.
• Define and implement mappings of large-scale inference workloads onto NVIDIA’s systems.
• Extend and integrate with NVIDIA’s SW ecosystem, contributing to libraries, tooling, and interfaces that enable seamless deployment of models across platforms.
• Benchmark, profile, and monitor key performance and efficiency metrics to ensure the compiler generates efficient mappings of neural network graphs to our inference hardware.
• Collaborate closely with hardware architects and design teams to feedback software observations, influence future architectures, and codesign features that unlock new performance and efficiency points.
• Prototype and evaluate new compilation and runtime techniques, including graph transformations, scheduling strategies, and memory/layout optimizations tailored to spatial processors.
• Publish and present technical work on novel compilation approaches for inference and related spatial accelerators at top tier ML, compiler, and computer architecture venues.

Requirements

• MS or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent experience, with 5 years of relevant experience.
• Strong software engineering background with proficiency in systems level programming (e.g., C/C++ and/or Rust) and solid CS fundamentals in data structures, algorithms, and concurrency.
• Hands on experience with compiler or runtime development, including IR design, optimization passes, or code generation.
• Experience with LLVM and/or MLIR, including building custom passes, dialects, or integrations.
• Familiarity with deep learning frameworks such as TensorFlow and PyTorch, and experience working with portable graph formats such as ONNX.
• Solid understanding of parallel and heterogeneous compute architectures, such as GPUs, spatial accelerators, or other domain specific processors.
• Strong analytical and debugging skills, with experience using profiling, tracing, and benchmarking tools to drive performance improvements.
• Excellent communication and collaboration skills, with the ability to work across hardware, systems, and software teams.
• Ideal candidates will have direct experience with MLIR based compilers or other multilevel IR stacks, especially in the context of graph based deep learning workloads.

Benefits

• Eligible for equity and benefits