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

Job Title

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

Role Summary

The engineer will develop algorithms and optimizations for NVIDIA's LPX inference and compiler stack, working at the intersection of large-scale systems, compilers, and deep learning.

The role focuses on mapping neural-network inference workloads to NVIDIA platforms, implementing compiler and runtime components, benchmarking performance, and collaborating with hardware and software teams to co-design efficient solutions.

Experience Level

Entry-level (new college graduate). Role targets recent or soon-to-be graduates but considers equivalent practical experience.

Responsibilities

The key responsibilities include designing, implementing, and evaluating compiler/runtime features and optimizations to enable high-performance inference on specialized hardware.

• Build and maintain high-performance runtime and compiler components focused on end-to-end inference optimization.
• Define and implement mappings of large-scale inference workloads to NVIDIA systems and hardware.
• Extend and integrate with NVIDIA software ecosystem: libraries, tooling, and deployment interfaces.
• Benchmark, profile, and monitor performance and efficiency metrics; use findings to improve code generation and scheduling.
• Collaborate with hardware architects and design teams to provide software feedback and co-design features that improve performance and efficiency.
• Prototype and evaluate compilation and runtime techniques, including graph transformations, scheduling strategies, and memory/layout optimizations for spatial processors.
• Document and present technical results at internal reviews and external venues when appropriate.

Requirements

Required technical skills and experience. Degree requirements are listed separately under Education Requirements.

• Strong software engineering skills and systems-level programming experience in C/C++ and/or Rust.
• Solid computer science fundamentals: data structures, algorithms, and concurrency.
• Hands-on experience with compiler or runtime development: IR design, optimization passes, or code generation.
• Experience with LLVM and/or MLIR (building custom passes, dialects, or integrations).
• Familiarity with deep learning frameworks (TensorFlow, PyTorch) and portable graph formats such as ONNX.
• Understanding of parallel and heterogeneous compute architectures (GPUs, spatial accelerators, domain-specific processors).
• Proven analytical and debugging skills; experience with profiling, tracing, and benchmarking tools to drive performance improvements.
• Effective communication and collaboration skills for cross-functional engineering work.

Nice-to-have:

• Direct experience with MLIR-based compilers or other multilevel IR stacks for graph-based deep learning workloads.
• Prior work on spatial or dataflow architectures, static scheduling, or pipeline/tensor parallelism at scale.
• Contributions to open-source ML frameworks, compilers, or runtime systems.
• Research publications or presentations at relevant conferences (PLDI, CGO, ASPLOS, ISCA, MICRO, MLSys, NeurIPS, etc.).
• Experience with large-scale AI distributed inference or training systems, including performance modeling and capacity planning.

Education Requirements

Pursuing or recently completed MS or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent practical experience.

About the Company

Company: NVIDIA

Headquarters: Santa Clara, California, USA

NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.