Pmts Software Applications Eng.

AMD AMD · Semiconductors · VANCOUVER, BC · Engineering

This role is for a Principal Member of Technical Staff (PMTS) Software Development Engineer in AMD's AI Enterprise applications and platform engineering organization. The engineer will design, build, and evolve secure, reliable AI infrastructure applications and tools for deploying, running, and scaling ML and LLM workloads on cloud and GPU infrastructure. The role involves full-stack engineering, technical and strategic leadership, influencing roadmap and architecture, and partnering with product and customers. Key responsibilities include owning backend services and user-facing applications for AI/GPU and platform workflows, setting architectural direction for Kubernetes toolchain, optimizing systems for efficiency and reliability, and embedding engineering excellence. The role also involves customer interaction, leading initiatives from requirements to shipped outcomes, and mentoring engineers. Experience with GPU workloads, ML/LLM training or inference pipelines, and multi-tenant platform concerns is highly desirable.

What you'd actually do

  1. Own and drive design of complex, data-heavy backend services and user-facing applications for AI/GPU and platform workflows (Kubernetes, APIs, integrations).
  2. Set and evolve architectural direction for Kubernetes toolchain, APIs and UIs enabling core AI workflows; ensure security, scalability, and maintainability under real customer load.
  3. Influence Enterprise AI roadmap through deep understanding of customer needs, market direction, and technical feasibility; translating pain into prioritized engineering work.
  4. Drive AI-first development practices and continuously promote engineering efficiency using modern agent-driven software development methods.
  5. Stay current on cloud-native, Kubernetes, GPU/ML workflows, and LLM infrastructure trends and apply pragmatic choices to our stack and practices.

Skills

Required

  • 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/TypeScript and a major UI framework (React or equivalent); solid API and service design.
  • Hands-on experience with relational and NoSQL 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.

Nice to have

  • Experience with GPU workloads, ML/LLM training or inference pipelines, or multi-tenant platform concerns.
  • Customer-facing or field-adjacent engineering: discovery, technical scoping, or success of complex B2B deployments.
  • Open-source or platform-ecosystem awareness (K8s operators, schedulers, observability stacks).
  • Prior technical leadership: architecture decisions, cross-team initiatives, and growing engineers.

What the JD emphasized

  • AI infrastructure applications and tools
  • ML and LLM workloads
  • Kubernetes toolchain, APIs and UIs enabling core AI workflows
  • GPU/ML training or inference pipelines
  • agent-driven software development methods

Other signals

  • design, build, and evolve secure, reliable AI infrastructure applications and tools
  • deploy, run and scale ML and LLM workloads on modern cloud and GPU infrastructure
  • Kubernetes toolchain, APIs and UIs enabling core AI workflows
  • GPU/ML training or inference pipelines