Senior Machine Learning Applications and Compiler Engineer

Nvidia

Confirmed live 2 days ago High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Santa Clara, CA
Salary: $152,000–$241,500 / yr
Posted: 60 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $226k

This role $197k

$137k most similar roles pay here $293k

This role pays less than 70% of similar roles. Most pay $196,750–$254,750 — the shaded band above. At the midpoint, this role pays about $197k versus about $226k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior Machine Learning Applications and Compiler Engineer

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As a Senior Machine Learning Applications and Compiler Engineer, LPX, you will join the team to develop algorithms and optimizations for the LPX inference and compiler stack. You will build and maintain high-performance runtime and compiler components while defining mappings of large-scale inference workloads onto NVIDIA systems. Your daily work involves integrating with the software ecosystem, benchmarking performance metrics, and collaborating with hardware architects to influence future architectures. You will prototype compilation techniques like graph transformations and memory optimizations for spatial processors. The role requires proficiency in C/C++, Rust, LLVM, and MLIR, along with experience in PyTorch, TensorFlow, and ONNX. You must possess a deep understanding of parallel and heterogeneous compute architectures, including GPUs and spatial accelerators, to solve complex problems involving neural network graph mapping and efficient inference execution on specialized hardware.

Skills

C++ Rust LLVM MLIR PyTorch TensorFlow ONNX Runtime Development Profiling Benchmarking Data Structures Algorithms

What you'll do

Develop and maintain high-performance runtime and compiler components for end-to-end inference optimization.
Map large-scale neural network workloads onto NVIDIA systems and hardware platforms.
Integrate with the NVIDIA software ecosystem to improve libraries, tooling, and deployment interfaces.
Benchmark and profile performance metrics to ensure efficient mapping of neural network graphs to hardware.
Collaborate with hardware architects to provide feedback and co-design features for future architectures.
Prototype new compilation techniques including graph transformations, scheduling strategies, and memory optimizations.
Publish technical research on novel compilation approaches at top-tier ML and architecture venues.

What we're looking for

MS or PhD in Computer Science, Electrical/Computer Engineering, or a related field is required.
Candidates must have 5 years of relevant experience.
Proficiency in systems-level programming using C/C++ and/or Rust is required.
Experience in compiler or runtime development including IR design, optimization passes, or code generation is required.
Experience with LLVM and/or MLIR, including building custom passes, dialects, or integrations, is required.
Familiarity with deep learning frameworks like TensorFlow and PyTorch and experience with ONNX is required.
A solid understanding of parallel and heterogeneous compute architectures such as GPUs or spatial accelerators is required.
Strong analytical skills using profiling, tracing, and benchmarking tools to drive performance improvements are required.

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