Manager, Software Architecture

at Nvidia

📍 Santa Clara, United States

USD 184,000-356,500 per year

MIDDLE SENIOR

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI Communication @ 6 Distributed Systems @ 6 Machine Learning Networking @ 6 People Management @ 5 Python @ 5 Rust @ 5

Lead and develop a team of systems and networking engineers building distributed AI communication systems—libraries, frameworks, and system integrations.
Setting the technical roadmap in partnership with principal engineers and architects, balancing near-term delivery with long-term research bets.
Creating a culture of technical excellence and open collaboration. Handling project planning, resource allocation, and delivery timelines across concurrent workstreams.

8+ overall years of software engineering experience with advanced knowledge in systems software, networking, or distributed systems.
3+ years of direct people management.
BS, MS, PhD or equivalent experience in Computer Science, Computer Engineering, or a related field.
Ability to scope a problem, set a plan, and deliver results in a fast-paced R&D environment.
Strong communication skills—comfortable speaking publicly, writing technical documents, and giving candid feedback.
Good understanding of computer architecture, memory hierarchies, DMA engines, and networking.
Proficiency in programming languages such as C, C++, Rust and Python.
Understanding of ML systems concepts—transformer architectures, KV cache mechanics, model parallelism, or distributed training and inference patterns.