Senior Machine Learning Applications and Compiler Engineer, LPX

Details

NVIDIA is seeking an engineer to develop algorithms and optimizations for its LPX inference and compiler stack. The role is at the intersection of large-scale systems, compilers, and deep learning, focusing on how neural network workloads map onto future NVIDIA platforms.

Responsibilities

• Build, develop, and maintain high-performance runtime and compiler components, with a focus on end-to-end inference optimization.
• Define and implement mappings of large-scale inference workloads onto NVIDIA systems.
• Extend and integrate with NVIDIA's software ecosystem, contributing to libraries, tooling, and interfaces that enable seamless model deployment across platforms.
• Benchmark, profile, and monitor performance and efficiency metrics to ensure the compiler generates 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 that improve performance and efficiency.
• Prototype and evaluate compilation and runtime techniques, including graph transformations, scheduling strategies, and memory and layout optimizations for spatial processors.
• Publish and present technical work on novel compilation approaches for inference and related spatial accelerators at leading machine learning, compiler, and computer architecture venues.

Requirements

• MS or PhD in Computer Science, Electrical or Computer Engineering, or a related field, or equivalent experience, with 5 years of relevant experience.
• Strong software engineering background with proficiency in systems-level programming such as C++, C, and/or Rust.
• Solid computer science 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 portable graph formats such as ONNX.
• Understanding of parallel and heterogeneous compute architectures, including GPUs, spatial accelerators, or other domain-specific processors.
• Strong analytical and debugging skills, including experience with profiling, tracing, and benchmarking tools.
• Excellent communication and collaboration skills, with the ability to work across hardware, systems, and software teams.
• Experience with MLIR-based compilers or other multilevel IR stacks, particularly for graph-based deep learning workloads, is ideal.

Preferred Qualifications

• Experience with spatial or dataflow architectures, including static scheduling, pipeline parallelism, or tensor parallelism at scale.
• Contributions to open-source machine learning frameworks, compilers, or runtime systems, particularly in performance or scalability.
• Research impact demonstrated through publications or presentations at venues such as PLDI, CGO, ASPLOS, ISCA, MICRO, MLSys, or NeurIPS.
• Experience with large-scale distributed AI inference or training systems, including performance modeling and capacity planning for multi-rack deployments.

Compensation and Benefits

• Base salary for Level 3: CAD 135,000–185,000 per year.
• Base salary for Level 4: CAD 170,000–220,000 per year.
• 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.