NVIDIA
AI computing
MachineLearningApplicationsandCompilerEngineer,LPXNewCollegeGrad2026
“Machine Learning Applications and Compiler Engineer, LPX - New College Grad 2026 at NVIDIA. Skills: Machine Learning Applications, Compiler Engineering, inference optimization, runtime components, compiler components, deep learning algorithms, neural network workloads, LLVM, MLIR. 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”
What You'll Achieve.
ensure the compiler generates efficient mappings of neural network graphs to our inference hardware; unlock new performance and efficiency points
Industry & Context.
analytical and debugging skills
What They're Looking For.
Must Have
software engineering background, systems level programming (e. g. , C/C++ and/or Rust), 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, 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, analytical and debugging skills, experience using profiling, tracing, and benchmarking tools to drive performance improvements, Excellent communication and collaboration skills, ability to work across hardware, systems, and software teams
Nice to Have
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
What You'll Do.
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
and interfaces that enable seamless deployment of models across platforms
and monitor key performance and efficiency metrics to ensure the compiler generates efficient mappings of neural network graphs to our inference hardware
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
and computer architecture venues
How You'll Work.
Team & Collaboration
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; work across hardware, systems, and software teams
Communication Scope
Excellent communication and collaboration skills; Publish and present technical work
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