Manager, Software Architecture
$184k - $357k • US, CA, Santa Clara
Posted 1d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
On-site
Categories
AI Infrastructure Engineer
About the job
NVIDIA is seeking a Software Engineering Manager for its applied research team within the Networking Systems & Software Architecture group. This role will focus on solving complex AI infrastructure challenges by building systems-level software that efficiently moves data between GPUs, nodes, and storage at the speed required for modern AI. The team's work spans low-level transport optimization, hardware-software co-design, and communication frameworks integrated into production AI stacks, with an expansion into emerging areas like quantum computing interconnects. The Engineering Manager will lead this team, driving execution and setting technical direction, while fostering an extraordinary engineering organization.
Responsibilities
- Lead and develop a team of systems and networking engineers building distributed AI communication systems, including libraries, frameworks, and system integrations.
- Collaborate with principal engineers and architects to create a technical roadmap, balancing near-term delivery with long-term research objectives.
- Cultivate a culture of technical excellence and open collaboration.
- Manage project planning, resource allocation, and delivery timelines across multiple workstreams.
Requirements
- 8+ years of overall software engineering experience with advanced knowledge in systems software, networking, or distributed systems.
- 3+ years of direct people management experience.
- BS, MS, PhD, or equivalent experience in Computer Science, Computer Engineering, or a related field.
- Ability to scope problems, create plans, and deliver results in a fast-paced R&D environment.
- Strong communication skills, including public speaking, technical writing, and providing 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 like transformer architectures, KV cache mechanics, model parallelism, or distributed training and inference patterns.
Benefits
- Equity
- Benefits