Senior Machine Learning Applications and Compiler Engineer

What you'll do

• Build and maintain high-performance runtime and compiler components for end-to-end inference optimization.
• Implement mappings of large-scale inference workloads onto NVIDIA systems.
• Integrate with NVIDIA’s software ecosystem to contribute to libraries, tooling, and deployment interfaces.
• Benchmark and profile performance metrics to ensure efficient neural network graph mappings to hardware.
• Provide software feedback to hardware architects to influence future architecture designs and features.
• Prototype new compilation techniques including graph transformations, scheduling strategies, and memory optimizations.
• Publish and present technical work on novel compilation approaches at top-tier ML and architecture venues.

What we're looking for

• MS or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent experience.
• 5 years of relevant experience.
• 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.
• Direct experience with MLIR based compilers or other multilevel IR stacks, especially in the context of graph based deep learning workloads (preferred).