Sr. Technical Program Manager - Training at Scale

Advanced Micro Devices, Inc•San Jose, CA

4d•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: AMD is seeking a Senior Technical Program Manager (TPM) to lead Training at Scale programs for AMD Instinct products. You will drive end-to-end execution of largescale AI training initiatives while owning multi-quarter planning, roadmap development, and the operating cadence that turns strategy into predictable delivery across the Training at Scale engineering portfolio. In this role, you will be a core partner to engineering leadership, ensuring that near-term execution strength is matched by clear long-term plans, OKR rigor, and early risk/decision management across an evolving opensource AI ecosystem. THE PERSON: The ideal candidate is a highly structured program leader with technical depth in AI training frameworks and distributed training at scale, comfortable operating in ambiguity and turning strategy into executable roadmaps. You communicate crisply at all levels, build alignment across cross functional teams, and proactively surface risks, tradeoffs, and decision points before they become delivery blockers You thrive in a fast-moving environment, bring a strong operating cadence (OKRs, reviews, dashboards), and can build durable planning mechanisms that reduce engineering overhead while improving delivery predictability.

Requirements

Technical fluency in AI/ML systems, including distributed training and scalability/performance considerations.
Handson familiarity with AI training frameworks/ecosystems (e.g., PyTorch, JAX) and related tooling.
Understanding of GPU compute software stacks and performance considerations; familiarity with AMD ROCm and/or NVIDIA CUDA
Experience working in opensource ecosystems (contributing, managing upstream dependencies, release planning, community/ecosystem coordination).
Track record of proactive risk management and executive level stakeholder communication in ambiguous environments.

Responsibilities

Own Training at Scale portfolio planning: translate strategy into a multi-quarter roadmap, quarterly plans, and measurable outcomes.
Establish and run an execution operating model (OKRs, program reviews, decision logs, dashboards) to drive rigor, transparency, and predictable delivery.
Drive end-to-end delivery of largescale AI training capabilities across cross functional engineering teams; manage scope, milestones, dependencies, and critical path.
Apply technical judgment to identify and manage architecture level tradeoffs, technical dependencies, and technical risk; proactively surface decision points and escalation paths.
Build alignment with engineering leadership and key stakeholders on priorities, sequencing, and resourcing.