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

NVIDIA•Us, CA

12d

About The Position

Our work at NVIDIA is dedicated towards a computing model focused on visual and AI computing. For two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics, with our invention of the GPU. The GPU has also shown to be spectacularly effective at solving some of the most complex problems in computer science. Today, NVIDIA’s GPU simulates human intelligence, running deep learning algorithms and acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. We are looking to grow our company and teams with the smartest people in the world and there has never been a more exciting time to join our team! NVIDIA is 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. This is your chance to be part of something outstandingly innovative!

Requirements

Pursuing or recently completed a MS or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent experience.
Possess software engineering background with familiarity 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.
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.

Nice To Haves

Direct experience with MLIR based compilers or other multilevel IR stacks, especially in the context of graph based deep learning workloads.
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.
Demonstrated research impact, such as publications or presentations at conferences like 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.

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 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 new 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.