Note: The job is a remote job and is open to candidates in USA. NVIDIA is a leading company in GPU innovation and development for AI and accelerated workloads. They are seeking a Principal System Architect to define and drive architecture for complex GPU products while collaborating with cross-functional teams to optimize performance and scalability.
Responsibilities
- Define and drive architecture for complex, high-volume GPU products, ensuring they meet our ambitious performance and scalability goals
- Perform and guide power and performance evaluation, trade-off assessments, and architectural modeling to identify optimal chip, package and system construction
- Lead improvements in architecture, methodology and tools to improve the scalability of our system, collaborating closely with cross-functional engineering teams
- Specify and optimize SoC subsystems such as memory architecture, test infrastructure and power management
- Collaborate with RTL, verification, physical design, firmware, and software teams to successfully implement and integrate system components
- Produce high-quality technical documentation of SoC architecture, specifications, and development trade-offs
- Provide technical leadership and mentorship to junior architects and engineers, encouraging a culture of excellence and innovation
Skills
- Over 15 years in SoC architecture development or similar technical leadership roles
- Proven track record of defining and delivering multiple high-volume SoC systems (such as CPU, GPU, modem, networking or similar)
- Proficiency in evaluating power/performance and architectural modeling in high-level programming languages
- Strong understanding of SoC system fundamentals, including memory hierarchy, coherency, clocking, power domains, boot and reset, test, and debug methodologies
- Hands-on experience with silicon bring-up, debug and tuning
- Excellent interpersonal, leadership, and collaboration skills, with the ability to influence across organizations
- Outstanding documentation, written, and verbal communication skills
- Master's degree (or equivalent experience) in a relevant subject area: Computer Science, Electrical Engineering or Computer Engineering
- Experience with GPU or AI accelerator architecture, including platform aspects, off-chip I/O technologies, and networked multi-GPU systems
- Knowledgeable in modern packaging technologies, and their costs and benefits
- Knowledgeable in AI workload characteristics
- Outstanding analytical and problem-solving skills with a focus on optimizing performance, power, area, and complexity
Benefits
- You will also be eligible for equity and benefits.
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