THE ROLE:As GPU Software Architect, you will provide technical leadership at the intersection of GPU architecture, multi-ASIC platform bring-up, and software enablement for next-generation GPU products. This is a "Software-First" architecture role: you will reimagine and redefine the end-to-end software libraries lifecycle as it spans across multiple ASICs to create a unified software fabric and process supporting development of software libraries on cutting edge hardware.
You will serve as a bridging authority between software architecture and the hardware ecosystem, ensuring that architectural intent translates into working, performant, and scalable solutions for partnerships established with software libraries teams.
This role is focused on leading a team focused on new GPUs and new product introductions, with accountability spanning early architecture definition, pre-silicon modeling, multi-ASIC bring-up strategy, and software readiness for emerging platforms.
THE PERSON:You are a deeply technical leader who thrives in ambiguous, first-of-kind environments. You combine strong architectural intuition with hands-on experience bringing up complex hardware platforms and enabling software stacks on new silicon.
You are comfortable operating across layers - from architecture trade-offs and interconnect topology down to firmware, drivers, and performance tooling - and you are trusted to make decisions when data is incomplete. You naturally connect and lead teams through technical credibility, clarifying intent, and reducing friction between hardware and software organizations.
KEY RESPONSIBILITIES: - Architecture & Platform Leadership: Provide technical leadership for GPU architecture decisions with direct impact on multi-ASIC platforms, interconnects, memory systems, and scalability. Translate architectural concepts into concrete platform requirements spanning ASIC, firmware, drivers, and software libraries.
- Multi-ASIC Bring-Up Strategy: Define and lead bring-up strategies for new GPU platforms, including strategies spanning multiple ASICs. Partner with silicon, systems, and software teams to identify risks early and drive mitigation plans from pre-silicon through first silicon.
- Hardware-Software Co-Design: Drive hardware/software interface definition, ensuring architecture choices support and reflect the drive towards performance and quality. Influence firmware, driver, runtime, and performance software design to align with architectural intent.
- Early Silicon Enablement: Act as a technical escalation point during early silicon bring-up, debugging complex cross-layer issues spanning hardware, firmware, and software. Guide the creation of diagnostics, validation tools, and bring-up workflows that scale across teams and products.
- Cross-Functional Technical Leadership: Work across architecture, design, verification, drivers, performance libraries, and product teams to ensure alignment. Provide technical mentorship and review, raising the overall effectiveness of teams working on new GPU platforms.
- Knowledge Capture & Reuse: Capture lessons learned from new product bring-up and translate them into reusable architecture patterns, best practices, and documentation.
- Leverages AI-assisted software development tools to accelerate the design, implementation, review, and documentation of complex software libraries. Establishes best practices for responsible use of AI assistance, including validation, review, and traceability of generated code and technical artifacts.
PREFERRED EXPERIENCE: - Deep experience in GPU, accelerator, or SoC architecture, including memory systems, interconnects, and scalability considerations. History of technical leadership across distributed, cross-functional engineering teams.
- Strong background in systems software, firmware, drivers, or performance software used to enable new silicon. Proven experience in hardware/software co-design, including defining interfaces and debugging cross-layer issues.
- Hands-on programming experience in C/C++ and Python. Familiarity with low-level debugging tools and workflows. Experience working with performance modeling, simulators, or early validation infrastructure.
- Applied experience using AI-assisted coding tools in professional software engineering workflows, including code generation, refactoring, test creation, documentation, and design exploration.
- Advanced degree in Computer Engineering, Electrical Engineering, Computer Science, or equivalent practical experience.
ACADEMIC CREDENTIALS: - Advanced degrees, such as M.Sc., M.Eng., Ph.D. are preferred
LOCATION: Santa Clara, California
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