We are now looking for a Senior Machine Learning Applications and Compiler Engineer! You will work at the intersection of large-scale systems, compilers, and deep learning, crafting how neural network workloads map onto future NVIDIA platforms. Build, develop, and maintain high-performance runtime and compiler components, focusing on end-to-end inference optimization.

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 recent 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.

Experience with LLVM and/or MLIR, including building custom passes, dialects, or integrations.

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.

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.

Experience with large-scale AI distributed inference or training systems, including performance modeling and capacity planning for multi rack deployments. #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 .NVIDIA uses AI tools in its recruiting processes.

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📌 Senior Machine Learning Applications And Compiler Engineer, Lpx (Toronto)
🏢 NVIDIA
📍 Toronto