Technical Product Manager - Enterprise AI

THE ROLE:

This role sits within AMD's Enterprise AI product organization, working at the intersection of AI software, infrastructure, and platform strategy. The teamis responsible forshaping and delivering enterprise-grade AI platforms that spanROCminfrastructure, Kubernetes orchestration, GPU virtualization, AI frameworks, and performance optimization.

In this role, you will work directly with senior product leadership and collaborate closely with cross-functional engineering teams to translate complex technical capabilities into cohesive, production-ready enterprise AI solutions. You will gain exposure to the full product lifecycle - from strategy and roadmap definition to execution, launch, and post-release iteration - while learning how large-scale AI platforms are built andoperatedin real enterprise environments.

This is a highly collaborative, fast-moving environment where curiosity,systemsthinking, and a willingness to learn are valued. The role offers hands-on experience across hardware-aware software stacks, modern cloud-native platforms, and enterprise AI use cases, with opportunities to grow technical depth and product leadership skills.

THE PERSON:

The ideal candidate is intellectually curious, technically grounded, and motivated to learn quickly in complex problem spaces. You enjoy working across disciplines, asking good questions, and turning ambiguity into clarity. You are comfortable engaging with engineers and stakeholders atdifferent levels, and you take ownership of outcomes even when the path forward is not fully defined.

You are detail-oriented but able to think at a systems level, balancing technical depth withproductperspective. You communicate clearly, listen actively, and value collaboration. You are excited by the challenge of building enterprise AI platforms that move beyond experimentation into real-world production use.

KEY RESPONSIBILITIES:

• Partner with Enterprise AI product leadership to support product strategy, roadmap development, and execution across AI/ML platforms and services.

• Work closely with cross-functional engineering teams (AI frameworks and libraries, Kubernetes and orchestration, GPU virtualization, model performance, andROCminfrastructure/software) to translate strategicobjectivesinto clear product requirements.

• Contribute to the end-to-end product lifecycle for assigned features or components, includingrequirementsdefinition, prioritization, release readiness, and post-launch evaluation.

• Collaborate with internal teams and partners to align technical dependencies, APIs, and ecosystem integrations.

• Engage with internal stakeholders and customers to gather feedback,validateuse cases, andinformiterative product improvements.

• Support go-to-market and enablement efforts through the development of technical collateral, demos, and internal documentation.

• Track progress,communicatestatus, and surface risks or trade-offs to cross-functional teams and leadership.

What success looks like over time:

• First 3 months: Build foundational understanding of the enterprise AI stack, product roadmap, and cross-functional workflows; contribute to requirements and planning discussions.

• 6 months: Independently own defined product areas or features, drive alignment across teams, and contribute meaningfully to roadmap execution.

• 12 months:Operateas a trusted product partner across engineering teams, influencing productdirectionand helping deliver enterprise-ready AI capabilities at scale.

PREFERRED EXPERIENCE:

• Experience or strong exposure to machine learning systems, AI model lifecycles, or AI/ML frameworks such asPyTorch, TensorFlow, ONNX, or similar.

• Familiarity with cloud-native technologies, such as Kubernetes, containerized workloads, or distributed systems.

• Exposure to GPU compute stacks, performance optimization, virtualization concepts, or infrastructure-aware software is a plus.

• Experience translating technical concepts into product requirements, documentation, or stakeholder communications.

• Participation in internships, research projects, open-source contributions, or industry projects related to software, AI/ML, or infrastructure.

ACADEMIC CREDENTIALS:

• Bachelor's degree preferred in Computer Science, Computer Engineering, Software Engineering, or a related technical field.

• Coursework or academic projects in machine learning, distributed systems, systemsprogramming, or scalable software architectures preferred.

ACADEMIC CREDENTIALS:

• Santa Clara, CA