Principal Deep Learning Communication Architect
Company websitenvidia.com (NVIDIA, opens in new tab)
Base salary $272,000 to $431,250
Employer-published annual base salary, USD. Advertised base range in Santa Clara, CA +1 more
You apply on the employer's own site. Six Figure Work does not handle applications.
- LocationSanta Clara, CA +1 more
- WorkplaceWorkplace not stated
- Status at last checkSource accepting applications when checked
- Last checked (45 min ago) · observed since
All places named
- Santa Clara, CA
- Austin, TX
About this job
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
What You’ll Be Doing:
Architecture Leadership: Define the long-term technical roadmap for communication libraries across NVIDIA’s next-generation platforms. You will ensure the seamless scaling of models to clusters comprising hundreds of thousands of nodes.
AI Communication Library Design: Lead the development of next-generation communication primitives and collective algorithms. This includes optimizing for heterogeneous interconnects such as NVLink, Spectrum-X (Ethernet), and Quantum-X (InfiniBand).
Application- Communication Library Co-Design: Partner with application developers to architect and implement specialized communication primitives. You will ensure that AI and HPC libraries—including NCCL, NIXL, NVSHMEM, UCC, and UCX—evolve to meet the requirements of trillion-parameter and Agentic AI.
Hardware/Software Co-Design: Collaborate with silicon Aarchitects and software engineers to influence hardware specifications for next-generation networking, ensuring they meet the evolving demands of trillion-parameter LLMs and Agentic AI.
Quantitative Modeling: Develop high-fidelity analytical models and simulators to predict system behavior under emerging workloads.
What We Need to See:
Ph.D. or M.S. in Computer Science, Electrical Engineering, or a related field (or equivalent experience), with 12+ years of industry experience in high-performance computing (HPC) or distributed deep learning.
Parallelism Expertise: Deep understanding of 3D parallelism (Data, Tensor, Pipeline) and advanced strategies including Context Parallelism, Expert Parallelism, and Zero Redundancy Optimizer (ZeRO) variants.
Technical Proficiency: Deep technical proficiency with NCCL, UCX, UCC, NVSHMEM, or MPI. Experience with RDMA, RoCE, and low-level InfiniBand verbs is required.
Inference & Serving: Advanced knowledge of high-throughput inference engines and schedulers, specifically TensorRT-LLM, vLLM, SGLang, and NVIDIA Dynamo.
GPU Architecture: Expert knowledge of the NVIDIA GPU memory hierarchy (HBM3e/HBM4, L2 cache) and CUDA programming models.
Ways to Stand Out from the Crowd:
Framework Development: Hands-on experience developing within Megatron-Core, DeepSpeed, or JAX/XLA, with an understanding of how these frameworks interact with low-level communication runtimes is a plus.
Significant upstream contributions to major open-source projects (e.g., PyTorch Distributed, KServe, or Ray).
A proven track record of deploying and optimizing models on NVIDIA platforms or similar rack-scale systems.
A strong portfolio of patents or papers in top-tier systems/architecture venues (e.g., ISCA, ASPLOS, NeurIPS, SC).
With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most hard-working and talented people in the world working for us. If you're creative and passionate about developing cloud services we want to hear from you!
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 272,000 USD – 431,250 USD.You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Source: NVIDIA on Workday · View original posting (opens in new tab)
Pay in context
This role's base floor ($272,000) is above 87% of the 1,502 Data & ML roles in California listed on this site; 7 others list the same floor. Their median floor is $189,000; the middle half runs from $159,256 to $229,900.
Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 6, 2026.
This listingPrincipal Deep Learning Communication Architect$272,000 to $431,250
Senior Director, Procurement Operations and Systems$272K to $414K
Principal Research Scientist, Synthetic Data Generation$272,000 to $431,250
Senior Manager, AI and HPC Cluster Engineering Enablement$272,000 to $488,750
Senior Engineering Manager, Object Storage - DGX Cloud$272,000 to $431,250
Principal Systems Software Engineer$272,000 to $431,250
Senior Manager, Developer Infrastructure and Build Engineering$272,000 to $431,250
Chart as a table
| Role | Published base |
|---|---|
| Principal Deep Learning Communication Architect (this listing) | $272,000 - $431,250 |
| Senior Director, Procurement Operations and Systems | $272K - $414K |
| Principal Research Scientist, Synthetic Data Generation | $272,000 - $431,250 |
| Senior Manager, AI and HPC Cluster Engineering Enablement | $272,000 - $488,750 |
| Senior Engineering Manager, Object Storage - DGX Cloud | $272,000 - $431,250 |
| Principal Systems Software Engineer | $272,000 - $431,250 |
| Senior Manager, Developer Infrastructure and Build Engineering | $272,000 - $431,250 |
Observed changes
- First observed on the employer's board
- Began meeting the published-salary criteria
- Observed since
- Employer-stated posting date
- 2026-10-30
Where this pay comes from
Location as the employer wrote it: US, CA, Santa Clara • US, TX, Austin • US, Remote
base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 272,000 USD – 431,250 USD.
- Method
- Employer text, read with fixed rules and re-verified
- Checked
- Rule set
- us-base-v2
The pay passage appears in the employer's own job text. Fixed, deterministic rules located it, and the passage below was checked against the stored copy of the posting.
What we verified
- The pay figures come from the employer's own listing, quoted exactly above.
- The pay is an annual base salary in US dollars with a minimum of at least $100,000.
- The role was publicly listed on the employer's job board when last checked.
We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.
