Senior Machine Learning Applications and Compiler Engineer, LPX

NVIDIA; Toronto; Hybrid; CAD 135,000 - 185,000; Full time

Benefits offered by this job

Equity

Benefits

Hybrid work

Job summary

NVIDIA in Toronto is seeking a Senior Machine Learning Applications and Compiler Engineer to advance the compiler stack and ML inference. You will shape how neural network workloads map onto NVIDIA platforms, balancing performance and accuracy across large-scale systems.

Work with LLVM/MLIR and modern runtimes, collaborate with hardware teams, and publish results at top venues. The role requires MS/PhD and 5+ years of relevant experience, strong C/C++/Rust, and deep learning framework

Qualifications

• 5+ years of relevant experience in ML, compilers, or systems.
• Strong programming background with C/C++/Rust.
• Hands-on experience with compiler/runtime development and IR design.
• Experience with LLVM and MLIR including passes or dialects.
• Familiarity with TensorFlow, PyTorch, and ONNX.
• Understanding of parallel and heterogeneous compute architectures.

Responsibilities

• Build and maintain high-performance runtime and compiler components for end-to-end inference optimization.
• Define mappings of large-scale inference workloads onto CUDA/NVIDIA platforms.
• Extend NVIDIA SW ecosystem with libraries and tooling for model deployment.
• Benchmark, profile, and optimize performance of generated neural network graphs.
• Collaborate with hardware teams to feedback software observations for future architectures.
• Prototype new compilation and runtime techniques, including graph transformations and memory optimizations.
• Publish and present technical work at top ML, compiler, and architecture venues.

Skills

5+ years experience

C/C++/Rust

systems programming

CS fundamentals

debugging/profiling

communication

Education

MS or PhD in CS/EE or related field

Tools

LLVM

MLIR

TensorFlow

PyTorch

ONNX

Job description

We are now looking for a Senior Machine Learning Applications and Compiler Engineer!

NVIDIA is seeking engineers to develop algorithms and optimizations for our 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. This is your chance to be part of something outstandingly innovative!

What you’ll be doing:

• 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.

What we need to see:

• 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 opensource 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.

#LI-Hybrid

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 135,000 CAD - 185,000 CAD for Level 3, and 170,000 CAD - 220,000 CAD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until March 27, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.