Senior Machine Learning Applications and Compiler Engineer, LPX

Details

We are seeking a Senior Machine Learning Applications and Compiler Engineer to develop algorithms and optimizations for the LPX inference and compiler stack. You will work at the intersection of large-scale systems, compilers, and deep learning, crafting how neural network workloads map onto future NVIDIA platforms.

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 software 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 inference hardware.
• Collaborate closely with hardware architects and design teams to feedback software observations, influence future architectures, and co-design 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.

Ways to stand out from the crowd

• Prior work on spatial or dataflow architectures, including static scheduling, pipeline parallelism, or tensor parallelism at scale.
• Contributions to open source ML frameworks, compilers, or runtime systems, particularly in areas related to performance or scalability.
• Demonstrated research impact, such as publications or presentations at conferences like PLDI, CGO, ASPLOS, ISCA, MICRO, MLSys, NeurIPS, or similar.
• Experience with large-scale AI distributed inference or training systems, including performance modeling and capacity planning for multi-rack deployments.

Other information

• Location: Toronto, Canada.
• Office policy: Hybrid (#LI-Hybrid).
• Base salary range: 135,000 CAD - 185,000 CAD for Level 3; 170,000 CAD - 220,000 CAD for Level 4.
• You will also be eligible for equity and benefits.
• Applications accepted at least until March 27, 2026.
• This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.