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About This Role
Overview:
WHAT YOU DO AT AMD CHANGES EVERYTHING
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.
Responsibilities:
THE ROLE:
AMD is seeking a Senior Product Manager to drive strategy and execution for ROCm, AMD’s open\-source GPU software stack, with a specific focus on large\-scale model inference on AMD Instinct™ and Radeon™ hardware. This is a key, central role sitting at the intersection of the open\-source community, high\-performance computing, and production AI deployment.
You will join a small but focused team of product managers whose shared remit is inference at scale and the frameworks that enable it. You will influence engineering roadmaps, represent AMD in open\-source communities and at conferences, and connect the needs of AMD’s most strategic customers with the direction of the open\-source ecosystem. AMD’s customers include some of the organizations deploying and serving the largest language models in the world.
THE PERSON:
You are a technically fluent product leader with deep roots in GPU computing, open\-source software, and AI inference infrastructure. You thrive on moving fast in a community\-driven environment while also navigating the nuanced requirements of enterprise\-scale customers. You are as comfortable reading a GitHub issue thread as you are presenting a roadmap to a VP of Engineering at a hyperscaler.
KEY RESPONSBILITIES:
Product Strategy \& Roadmap
- Own the product roadmap for ROCm’s inference software capabilities, including integrations with key frameworks like PyTorch and JAX, serving runtimes like vLLM and SGLang, and the libraries and profiling tools that make inference workloads perform well on AMD hardware.
- Define and communicate a coherent strategy for how AMD software enables production inference workloads, covering the full range from single\-GPU to rack\-scale deployments, with a focus on developer experience, performance portability, and competitive standing.
- Identify when emerging OSS model architectures, serving runtimes, or inference techniques require an AMD software response and drive those requirements into the roadmap.
- Bring a strategic lens to inference by continuously evaluating the latest research, technology trends, and evolving deployment needs, ensuring the organization stays ahead of future inference requirements and translates market signals into actionable product strategy.
Open\-Source Community Engagement
- Serve as AMD’s active presence in the open\-source AI/ML community: monitor GitHub repositories, Discord servers, developer blogs, and academic papers to track emerging trends, pain points, and opportunities.
- Own and communicate AMD’s open\-source software roadmap, publishing updates across key community projects to build awareness, solicit feedback from model developers and researchers, and signal AMD’s long\-term direction to the open\-source ecosystem.
- Build and maintain relationships with key OSS maintainers, foundation working groups, and community contributors whose work shapes how AMD hardware is perceived and adopted.
- Represent AMD at major conferences such as SC, PyTorch Conference, and MLSys; write and review technical blog posts and community announcements on the AMD ROCm blog.
Engineering Partnership \& Execution
- Partner with engineering leads across inference libraries, kernel, runtime, and serving integration teams to translate developer and customer needs into prioritized engineering work.
- Drive release planning across rapid release cadences with a CI/CD\-first mindset and strong attention to compatibility, regression, and ecosystem readiness.
- Provide software\-informed feedback to hardware teams on future GPU architecture decisions, particularly where features relevant to inference workloads affect serving performance at scale.
Customer \& Partner Engagement
- Balance the signal from large customers, who may need specific optimizations or bespoke integrations, against the needs of the broader open\-source community, who value standards, portability, and low friction.
- Collaborate with ecosystem partners such as Hugging Face, Red Hat, and cloud providers on joint integrations and AMD\-supported software solutions.
PREFERRED EXPERIENCE:
- Proven years of product management experience in a software\-focused role, ideally developer tools, GPU computing, or AI/ML infrastructure.
- Deep familiarity with at least one of PyTorch, JAX, or Triton: how they are structured, how they dispatch work to hardware backends, and what the end\-to\-end developer experience looks like.
- Practical understanding of LLM inference, including attention mechanisms, KV cache design, quantization strategies such, continuous batching, speculative decoding, and parallel serving across multiple GPUs.
- Hands\-on familiarity with GPU programming at some level, whether HIP, CUDA, or Triton kernels, with the ability to read kernel\-level code and benchmark output even if not writing it day to day.
- Strong written communication skills across technical documentation, roadmap narratives, community blog posts, and executive\-facing strategy documents.
- Prior experience at a GPU vendor, cloud provider AI infrastructure team, or frontier AI lab.
- Experience contributing to or managing products in an open\-source context: GitHub workflows, community governance, and OSS release management.
- Contributions in code, issues, or documentation to major open\-source inference projects such as vLLM, SGLang, Triton, or PyTorch.
- Familiarity with AMD’s hardware portfolio: Instinct MI\-series for data center compute and Radeon for workstation and consumer GPU compute.
ACADEMIC CREDENTIALS:
- Bachelor’s degree in Computer Science, Electrical Engineering, or a related technical field. Advanced degree a plus but not required given equivalent experience.
LOCATION:
- Bay Area · Austin · Remote
This role is not eligible for visa sponsorship.
\#LI\-EV1
\#LI\-HYBRID
Qualifications:
*Benefits offered are described:* AMD benefits at a glance. *AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee\-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third\-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.* *AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available* *here.* *This posting is for an existing vacancy.*
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At AMD, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
AMD AI Hiring
AMD has 19 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, Research Scientist. Positions span Austin, TX, US, Secaucus, NJ, US, San Jose, CA, US. Compensation range: $244K - $244K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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