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

We are 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.

Key responsibilities include:

• Building, developing, and maintaining high-performance runtime and compiler components, focusing on end-to-end inference optimization.
• Defining and implementing mappings of large-scale inference workloads onto NVIDIA's systems.
• Extending and integrating with NVIDIA's SW ecosystem, contributing to libraries, tooling, and interfaces that enable seamless deployment of models across platforms.
• Benchmarking, profiling, and monitoring key performance and efficiency metrics to ensure the compiler generates efficient mappings of neural network graphs to our inference hardware.
• Collaborating closely with hardware architects and design teams to feedback software observations, influence future architectures, and codesign features that unlock new performance and efficiency points.
• Prototyping and evaluating new compilation and runtime techniques, including graph transformations, scheduling strategies, and memory/layout optimizations tailored to spatial processors.
• Publishing and presenting technical work on novel compilation approaches for inference and related spatial accelerators at top-tier ML, compiler, and computer architecture venues.

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.