Software Development Engineer

ADVANCE YOUR CAREER. ADVANCE THE WORLD.

At AMD, we believe technology can change lives for the better. It can heal us, entertain us, and make us more connected, productive, and understanding of the world around us. And we’re looking for talent who feel the same: people who want to leave the planet better than they found it, those who don’t shy away from humanity’s challenges but are determined to help solve them.

AMD is powering the next generation of supercomputing, high-performance computing, cloud, and AI. Whether you’re designing next-gen processors, enabling AI breakthroughs, or creating go-to-market plans, every role at AMD contributes to something bigger — technology that moves the world forward.

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 library 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.

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.

• 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.
• Leverage AI‑assisted software development tools to accelerate the design, implementation, review, and documentation of complex software libraries. Establish best practices for responsible use of AI assistance, including validation, review, and traceability…