Principal 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.

AMD is seeking a Principal Software Development Engineer to join the AMD Silo AI Enterprise applications and platform engineering organization. You will design, build, and evolve secure, reliable AI infrastructure applications and tools that help customers deploy, run and scale ML and LLM workloads on modern cloud and GPU infrastructure.

At this level you combine hands‑on full-stack engineering with technical and strategic leadership: you influence roadmap and architecture, partner with product and customers, and raise the bar for quality, operability, and developer experience across teams. Tech context (indicative):
Kubernetes, Python, Go, relational and document data stores (e.g. PostgreSQL, MongoDB), AMD GPU hardware, CI/CD and observability.

Engineering & architecture

• Own and drive design of complex, data-heavy backend services and user-facing applications for AI/GPU and platform workflows (Kubernetes, APIs, integrations).
• Set and evolve architectural direction for Kubernetes toolchain, APIs and UIs enabling core AI workflows; ensure security, scalability, and maintainability under real customer load.
• Optimize systems for efficiency, performance and reliability; debug and resolve cross-cutting production issues.
• Embed engineering excellence: code quality, automated testing, CI/CD, observability, and API/design standards across the teams you work with.

Product, customers & agility

• Influence Enterprise AI roadmap through deep understanding of customer needs, market direction, and technical feasibility; translating pain into prioritized engineering work.
• Interface with customers (and sales/customer success as needed): clarify requirements, manage expectations, and feed learnings back into the product/engineering loop.
• Lead initiatives from evolving requirements to shipped outcomes—decompose large problems into phases, owners, and measurable milestones.

Leadership & collaboration

• Lead technically and strategically across teams and drive complex issues (technical and organizational) to resolution.
• Drive AI-first development practices and continuously promote engineering efficiency using modern agent‑driven software development methods.
• Represent engineering in roadmap and trade‑off discussions with product, design, and platform, and align delivery with business and UX goals.
• Mentor and uplift engineers via design reviews, code reviews, and coaching to grow technical judgment and execution quality of the team.
• Stay current on cloud‑native, Kubernetes, GPU/ML workflows, and LLM infrastructure trends and apply pragmatic choices to our stack and practices.

• Proven track record as a senior/principal-level full-stack or backend-leaning software engineer in cloud-deployed, data-intensive web applications.
• Strong Python and modern JavaScript/Type Script and a major UI framework (React or equivalent); solid API and service design.
• Hands‑on experience with relational and No
  
  SQL databases, Git, and Kubernetes (or equivalent orchestrated environments).
• Ability to work across functions (product, design, platform, GTM) with clear communication and stakeholder management.
• Agile delivery experience; comfort owning end‑to‑end design -> implementation -> deployment -> support.
• Initiative, ownership, and collaboration; pragmatic problem‑solving under constraints.
• Experience with GPU workloads, ML/LLM training or inference pipelines, or multi‑tenant platform concerns.
• Customer‑fac…