Senior Engineer - GPU Software Architect

Advanced Micro Devices, Inc

41d•Hybrid

About The Position

At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. THE ROLE: As GPU Software Architect, you will be a key technical contributor working at the intersection of GPU architecture, multi‑ASIC platform enablement, and software libraries for next‑generation GPU products. This is a software‑first architecture role, where you will contribute to shaping how GPU software libraries are designed, brought up, and validated across new hardware platforms. You will work closely with principal architects, hardware teams, and software engineers to ensure architectural intent is translated into working, performant, and maintainable software solutions. This role is focused on new GPUs and new product introductions, with hands‑on involvement spanning early platform enablement, software bring‑up, and cross‑team integration. THE PERSON: You are a strong technical engineer who thrives in complex, evolving environments. You have hands‑on experience enabling software on new hardware and are comfortable working across abstraction layers — from architecture concepts to implementation and debugging. You enjoy collaborating with hardware, firmware, and software teams, and you are effective at translating architectural ideas into practical software solutions. You seek clarity, learn quickly from early data, and contribute constructively when requirements are incomplete. You grow influence through technical execution, collaboration, and mentoring, and you are motivated to expand your architectural scope over time.

Requirements

Solid experience in GPU, accelerator, or systems software, with exposure to new hardware enablement.
Background in systems software, performance libraries, firmware, or drivers used to support GPU platforms.
Practical experience with hardware/software co‑design, including interface definition and cross‑layer debugging.
Strong programming skills in C/C++ and Python, with familiarity in debugging low‑level or performance‑sensitive code.
Experience working with performance analysis tools, simulators, or early validation environments.
Familiarity with collaborative, open‑source or large‑scale software development practices.
Applied experience using AI‑assisted coding tools in professional software workflows.

Nice To Haves

Advanced degrees, such as M.Sc., M.Eng., Ph.D. are preferred

Responsibilities

Software Architecture & Enablement: Contribute to GPU software architecture efforts that support new hardware platforms and multi‑ASIC systems. Translate architectural guidance into concrete software designs and implementation plans across GPU libraries.
Platform Bring‑Up Support: Participate in early platform and silicon bring‑up activities, working with hardware, firmware, and driver teams to identify and resolve issues. Assist in developing diagnostics, validation tools, and workflows that support scalable bring‑up and debugging.
Hardware–Software Collaboration: Work with hardware and software architects to refine hardware/software interfaces and ensure software readiness for new GPUs.Provide feedback from software bring‑up and performance analysis to inform architectural decisions.
Technical Execution & Review: Draft and review technical designs that consider performance, scalability, and roadmap constraints. Execute complex software work independently while collaborating with peers to meet delivery commitments.
Knowledge Sharing & Mentorship: Document lessons learned from new product enablement and contribute to shared best practices. Coach and mentor engineers within the normal software development lifecycle and agile practices.
AI‑Assisted Development: Apply AI‑assisted software development tools to accelerate implementation, testing, review, and documentation, following established validation and traceability practices.