Principal Developer, AI Networking

Nvidia

Confirmed live yesterday High trust

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CATXCOWA
Salary: $272,000–$431,250 / yr
Posted: 91 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $214k

This role $352k

$121k most similar roles pay here $465k

This role pays more than 97% of similar roles. Most pay $170,902–$256,500 — the shaded band above. At the midpoint, this role pays about $352k versus about $214k for comparable roles.

Based on 239 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 · Principal Developer, AI Networking

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Principal Developer, AI Networking joins the AI Networking Codesign and Benchmarking R&D group to profile, analyze, and optimize AI workloads on large-scale GPU and CPU clusters for distributed Deep Learning LLM training and inference. The role focuses on collective communication and networking across hardware components like HCAs, Switches, CPUs, GPUs, and Systems, while engaging with software layers including machine learning frameworks and computing libraries. Key responsibilities include building performance analysis tools, defining test plans, and developing PyTorch trace-based profiling toolsets to identify bottlenecks in high-performance networking. The candidate will utilize Python, Bash, C++, CUDA, NCCL, RoCE, RDMA, MPI, and SHARP. This position addresses the technical challenges of distributed systems and large-scale LLM workloads by providing performance insights and co-designing network systems to meet specific performance targets for advanced AI infrastructure.

Skills

NCCL RDMA CUDA PyTorch TensorFlow C++ Python Bash RoCE MPI SHARP Distributed Systems LLM Training High-Performance Networking Profiling Benchmarking

What you'll do

Profile, analyze, and optimize AI workloads on large-scale GPU and CPU clusters for distributed LLM training and inference.
Characterize deep learning models and workloads specifically aimed at high-performance networking and NVIDIA communication libraries.
Identify performance bottlenecks and areas for improvement within collective communications and network infrastructure.
Develop a PyTorch trace-based profiling, analysis, and replaying toolset to aid in benchmarking and debugging.
Define performance test plans and set expectations for new technologies to ensure they meet specific performance targets.
Build performance analysis tools and strategies to clarify system limitations and hardware/software interactions.
Analyze the performance of various components including HCAs, switches, CPUs, GPUs, and memory systems.

What we're looking for

Bachelor's degree in Computer Science, Software Engineering, or equivalent experience.
15+ years of experience with high-performance networking including RDMA, MPI, NCCL, and SHARP.
Proficiency in programming languages including Python, Bash, and C++.
Experience with NVIDIA GPUs and the CUDA library.
Expertise in networking collective communication libraries such as NCCL and protocols like RoCE and RDMA.
Knowledge of deep learning frameworks such as TensorFlow or PyTorch.
Demonstrated ability in performance evaluation techniques and tools to identify bottlenecks.
Experience in container-based development environments.

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