Principal Software Engineer – Csp Engagements

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Principal Software Engineer role at NVIDIA focused on Data Center Systems and Software CSP engagements, driving system software architecture, technical deep dives, and resolving complex customer issues for NPI projects. Requires extensive experience in scalable server systems, SW/HW interface, computer architecture, and low-level hardware/software interfaces.

What you'd actually do

  1. Drive system software architecture alignment and technical deep dives, acting as the primary software engineering contact for NPI projects with key customers.
  2. Collaborate with major customers to understand their roadmap, use cases, and requirements, aligning them with NVIDIA’s roadmap.
  3. Spearhead cross functional efforts to resolve complex and high-profile customer issues during NPI phase.
  4. Make key technical decisions even when faced with ambiguity and mitigate execution risks by following left shift strategy.
  5. Build and maintain customer trust by understanding and addressing their needs.

Skills

Required

  • Extensive experience in designing scalable, high-performance server systems at the SW/HW interface.
  • Proven leadership skills with strong project ownership in complex software and hardware environments.
  • Deep understanding of computer architecture, microprocessor concepts, and expert knowledge of ARM (aarch64) and x86 architectures.
  • Proficient in system software design, OS fundamentals, Linux kernel device drivers, and low-level hardware/software interfaces.
  • Skilled in complex system-level debugging, performance analysis, and test design.
  • BS or MS in Computer Engineering, Computer Science, or related field, or equivalent experience with over 15 years in system software architecture and development.

Nice to have

  • Knowledge of cloud and cluster level deployment and management systems
  • Expertise in Out of Band and In-band management architectures.
  • Experience with GPU computing (CUDA), deep learning workloads
  • Knowledge of Memory fabric and CXL architectures

What the JD emphasized

  • over 15 years in system software architecture and development