Machine Learning Applications and Compiler Engineer, LPX - New College Grad 2026

Details

Our work at NVIDIA is dedicated towards a computing model focused on visual and AI computing. For two decades, NVIDIA has pioneered visual computing and the GPU, which now runs deep learning algorithms and powers systems that perceive and understand the world. NVIDIA is seeking engineers 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 for model deployment across platforms.
• Benchmark, profile, and monitor performance and efficiency metrics to ensure efficient mappings of neural network graphs to inference hardware.
• Collaborate with hardware architects and design teams to provide software feedback, influence future architectures, and co-design features for performance and efficiency.
• Prototype and evaluate compilation and runtime techniques, including graph transformations, scheduling strategies, and memory/layout optimizations for spatial processors.
• Publish and present technical work on compilation approaches for inference and spatial accelerators at top-tier ML, compiler, and architecture venues.

Requirements

• Pursuing or recently completed an MS or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent experience.
• Software engineering background with familiarity in systems-level programming (e.g., 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.
• 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 to work across hardware, systems, and software teams.
• Ideal: direct experience with MLIR-based compilers or other multilevel IR stacks for graph-based deep learning workloads.

Ways to Stand Out

• 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, especially related to performance or scalability.
• Demonstrated research impact (publications/presentations at 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.

Compensation & Benefits

• Base salary ranges (location-, experience-, and level-dependent):
  • Level 3: 135,000 CAD - 185,000 CAD
  • Level 4: 170,000 CAD - 220,000 CAD
• Eligible for equity and company benefits (link provided in posting).

Additional Information

• Applications will be accepted at least until May 9, 2026.
• This posting is for an existing vacancy.
• NVIDIA uses AI tools in its recruiting processes.