Principal Developer, AI Networking

Nvidia

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CA
Salary: $272,000–$431,250 / yr
Posted: 4 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $200k

This role $352k

$132k most similar roles pay here $463k

This role pays more than 99% of similar roles. Most pay $163,987–$236,675 — the shaded band above. At the midpoint, this role pays about $352k versus about $200k 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 980 open roles on FindRole.

Listed pay typically runs $168,000–$270,250 across 966 roles with salary data.

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View all roles at Nvidia

At a glance

TL;DR · Principal Developer, AI Networking

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As a senior software engineer in the AI Networking Codesign and Benchmarking R&D group, you will focus on profiling, analyzing, and optimizing AI workloads on large-scale GPU and CPU clusters used for distributed Deep Learning LLM training and inference. Your day-to-day responsibilities include characterizing AI workloads, benchmarking performance to identify bottlenecks, developing PyTorch trace-based profiling tools, and collaborating with hardware and software teams to provide critical insights into system performance. You will need extensive experience in high-performance networking technologies like RDMA, MPI, NCCL, and SHARP, along with proficiency in Python, Bash, and C++. Additionally, expertise in NVIDIA GPUs, CUDA, PyTorch, and deep learning frameworks is essential for this role, which operates at the cutting edge of AI innovation.

Skills

Python C++ Bash PyTorch CUDA NCCL RDMA MPI RoCE NVIDIA_GPUs Intel_CPUs AMD_CPUs ARM_CPUs HCA Docker TensorFlow Prometheus Grafana CI/CD

What you'll do

Profile and analyze AI workloads on large-scale GPU clusters for LLM training.
Identify performance bottlenecks in distributed systems with a focus on networking.
Develop PyTorch-based profiling tools to optimize network system performance.
Define performance test plans and set expectations for new technologies.
Engage with hardware and software teams to provide performance analysis insights.

What we're looking for

Over 15 years of experience in high-performance networking technologies (RDMA, MPI, NCCL).
Expertise in performance evaluation techniques and deep learning frameworks (TensorFlow, PyTorch).
Proficiency with NVIDIA GPUs, CUDA library, and collective communication libraries like NCCL.
Strong analytical skills and hands-on experience with AI workloads and benchmarking for LLM training.
Experience developing tools using Python, Bash, and C++ in a container-based environment.
Comprehensive knowledge of system components including CPUs (Intel/AMD/ARM), GPUs, HCA, memory, PCI.
Ability to collaborate effectively across hardware and software teams.

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