Senior Machine Learning Applications and Compiler Engineer, LPX

Details

We are seeking a Senior Machine Learning Applications and Compiler Engineer to develop algorithms and optimizations for the LPX inference and compiler stack. You will work at the intersection of large-scale systems, compilers, and deep learning to map neural network workloads onto NVIDIA platforms.

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 software ecosystem, contributing to libraries, tooling, and interfaces for model deployment across platforms.
• Benchmark, profile, and monitor performance and efficiency metrics to ensure efficient mappings of neural network graphs to inference hardware.
• Collaborate with hardware architects and design teams to provide software feedback, influence future architectures, and codesign features to improve performance and efficiency.
• Prototype and evaluate compilation and runtime techniques, including graph transformations, scheduling strategies, and memory/layout optimizations for spatial processors.
• Publish and present technical work on compilation approaches for inference and spatial accelerators at ML, compiler, and architecture venues.

Requirements

• MS or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent experience, with 5 years of relevant experience.
• Strong software engineering background with 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 (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 to work across hardware, systems, and software teams.
• Ideal candidates will have direct experience with MLIR-based compilers or other multilevel IR stacks, especially for graph-based deep learning workloads.

Ways to stand out

• Prior work on spatial or dataflow architectures, including static scheduling, pipeline parallelism, or tensor parallelism at scale.
• Contributions to open-source ML frameworks, compilers, or runtime systems, particularly related to performance or scalability.
• Demonstrated research impact (publications or presentations at 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.

Compensation & Benefits

• Base salary ranges (location- and level-dependent):
  • Level 3: 152,000 USD - 241,500 USD
  • Level 4: 184,000 USD - 287,500 USD
• Eligible for equity and benefits (link to benefits provided in original posting).

Additional information

• Office policy: #LI-Hybrid (hybrid role).
• Application deadline: Applications accepted at least until March 23, 2026.
• NVIDIA uses AI tools in recruiting processes and is an equal opportunity employer committed to diversity and inclusion.