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About This Role
Overview: ADVANCE YOUR CAREER. ADVANCE THE WORLD. At AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.
Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career. Responsibilities: THE ROLE:
Define and drive the product strategy for ROCm AI, AMD’s AI-native developer experience that simplifies how developers discover, set up, use, manage, and troubleshoot AMD AI software. You will shape an integrated experience spanning AMD Skills, ROCm CLI, ROCm Console, ROCm Hyperloom, and emerging agentic workflows that make AI on AMD faster to build, easier to use, and simpler to optimize.
You will work across engineering, architecture, developer relations, product marketing, open-source communities, and strategic customers to identify target personas and their highest-value problems, validate product-market fit, and translate insights into product requirements, roadmap priorities, release plans, ecosystem integrations, and go-to-market programs. Success will be measured by developer adoption and productivity, time to first successful workload, workflow completion, product quality and repeat usage.
THE PERSON:
The ideal candidate is a technically strong product leader who understands how developers evaluate, adopt, and use AI software. You combine customer empathy and product judgment with the technical depth to engage engineering teams on AI frameworks, model serving, command-line tools, observability, compatibility, and agentic development workflows.
You can move from a developer or customer problem to a clear product requirement, prioritized roadmap, launch plan, and measurable outcome. You communicate crisply, exercise sound judgment amid ambiguity, and align cross-functional teams without direct reporting authority. You are passionate about creating an intuitive and trusted developer experience across cloud infrastructure, enterprise systems, workstations, desktops, and AI PCs.
KEY RESPONSIBILITIES:
- Define the product vision, strategy, roadmap, positioning, and success metrics for ROCm AI and its integrated developer experience.
- Define and segment core personas and translate their pain points into prioritized product journeys.
Establish and continuously validate product market fit through customer discovery, design partnerships, pilots, product telemetry, adoption data, support trends, community signals, and structured feedback.
- Own product requirements and prioritization across AMD Skills, ROCm CLI, ROCm Console, and emerging AI-assisted workflows, creating cohesive experiences from software discovery and setup through deployment, diagnostics, and ongoing usage.
- Partner with engineering and architecture teams to align component roadmaps, define release criteria, and deliver a consistent and reliable user experience.
- Define product versioning, compatibility, release cadence, and lifecycle requirements for independently released ROCm AI components across AMD Instinct™, AMD Radeon™, and supported AMD Ryzen™ platforms..
- Own market and competitive analysis for AI developer tools, agentic workflows, platforms and identify ecosystem gaps and differentiated opportunities for AMD.
- Develop the ecosystem strategy for integrating ROCm AI with coding agents, development environments, marketplaces, CI/CD systems, open-source projects, and enterprise platforms.
- Partner with developer relations, documentation, solutions architecture, support, marketing, sales, and business development to deliver product launches, technical content, demos, and go-to-market programs.
- Represent ROCm AI in customer briefings, executive reviews, developer events, industry forums, and open-source communities, while developing partnerships that expand adoption and improve the developer experience.
PREFERRED EXPERIENCE:
- Proven years of experience in product management, technical product management, software product strategy, developer tools, AI/ML infrastructure, cloud platforms, or related fields.
- Demonstrated ownership of a technical product from customer discovery and product definition through launch, adoption, and lifecycle management.
- Experience defining personas, conducting customer research, and validating product-market fit for technical products.
- Experience building products for developers, ML engineers, platform teams, MLOps teams, data scientists, or enterprise infrastructure operators.
- Strong understanding of AI development workflows, including environment setup, model deployment, model serving, diagnostics, observability, and software lifecycle management.
- Familiarity with AI frameworks and serving technologies such as PyTorch, TensorFlow, JAX, ONNX Runtime, vLLM, SGLang, or similar technologies.
- Experience with command-line tools, SDKs, APIs, developer consoles, telemetry, diagnostics, or systems-management products.
- Experience with AI coding assistants, autonomous agents, agent skills, tool use protocols, and AI-assisted software-development workflows.
- Experience defining software compatibility policies, release cadences, versioning strategies, support matrices, packaging, and lifecycle requirements.
- Experience working with open-source software and using GitHub activity, community feedback, issues, and contributions to guide product decisions.
- Experience conducting market and competitive analysis, identifying product differentiation, and translating findings into roadmap priorities and positioning.
- Strong analytical skills and experience defining product metrics, analyzing adoption funnels, and using data to prioritize investments.
- Ability to translate complex technical concepts into clear product requirements, customer value propositions, go-to-market plans, and executive communications.
- Demonstrated ability to influence engineering, architecture, developer relations, marketing, sales, and business development roadmaps without direct reporting authority.
- Excellent written, verbal, presentation, and stakeholder management skills.
- Experience with AMD ROCm™, HIP, GPU software, Linux, containers, Kubernetes, or model serving platforms is preferred.
- Experience working with cloud service providers, AI companies, server OEMs, software partners, or enterprise customers is a plus.
- Hands-on familiarity with Python, shell scripting, GitHub workflows, or Linux-based AI development environments is a plus.
ACADEMIC CREDENTIALS:
- B.S. or M.S. in Computer Science, Computer Engineering, Electrical Engineering, Business, or a related field. Equivalent practical experience will be considered.
LOCATION:
- San Jose, CA preferred.
- Other locations may be considered.
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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/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At AMD, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
AMD AI Hiring
AMD has 16 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span Austin, TX, US, Santa Clara, CA, US, San Jose, CA, US.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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