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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 visionary Head of AI Engineering Productivity to lead AI enablement and accelerate the adoption of agentic systems across software and hardware engineering workflows. This role sits at the apex of AMD’s cluster innovation, driving step\-function agentic improvements in how software is built, hardware is validated, and decisions are made. THE PERSON:
The ideal candidate is a visionary leader who can define and execute a roadmap to operationalize cutting\-edge AI across engineering, cluster validation, and delivery, turning emerging technologies into scalable, high\-impact systems that power productivity. They thrive in highly collaborative environments and partner deeply with engineering, research, product, and business teams to embed AI into daily workflows, unlock new capabilities, and help reshape how AMD brings clusters to some of the most exciting businesses in the world. KEY RESPONSIBILITIES:
- Define and execute AMD’s AI enablement strategy across software engineering, hardware development, validation, operations, and business functions.
- Lead the adoption of AI copilots, autonomous agents, and intelligent workflows throughout the engineering lifecycle.
- Deploy agentic systems that autonomously triage issues, route work, and propose solutions across large\-scale engineering environments.
- Build scalable AI platforms, APIs, frameworks, and services that accelerate safe and effective AI adoption across teams.
- Establish best practices for AI model integration, evaluation, deployment, governance, and operationalization.
- Develop reusable agent frameworks that support tool orchestration, memory management, workflow automation, and customization.
- Identify opportunities to replace manual processes with intelligent, AI\-driven workflows that improve efficiency and scalability.
- Drive measurable improvements in engineering productivity, developer velocity, hardware validation cycles, and organizational effectiveness.
- Enable AI\-powered capabilities such as documentation generation, onboarding assistants, knowledge retrieval, and workflow automation.
- Transform internal support functions through conversational agents capable of executing tasks across business operations.
- Partner with engineering, research, infrastructure, and product leaders to identify priorities and accelerate AI adoption.
- Collaborate with external partners, model providers, infrastructure vendors, and open\-source communities to incorporate best\-in\-class technologies.
- Optimize AI platform performance, inference efficiency, and operational cost at scale.
- Evaluate emerging AI ecosystems, multimodal models, development tools, and agentic technologies to guide strategic investments.
PREFERRED EXPERIENCE:
- Deep expertise in AI/ML systems, Large Language Models (LLMs), agentic architectures, modern AI tooling, AI\-native developer workflows, AI copilots, autonomous agents, and intelligent workflow systems.
- Hands\-on experience with agent frameworks, tool use, orchestration systems, memory architectures, evaluation systems, model integration, model evaluation, and model deployment.
- Experience delivering platform\-level products and production\-ready AI solutions within large\-scale engineering organizations.
- Expertise implementing intelligent automation solutions, including automated bug triage, issue routing, workflow automation, intelligent task execution, AI\-powered documentation generation, onboarding assistants, knowledge retrieval systems, and conversational AI.
- Experience optimizing inference performance, AI infrastructure, scalability, operational efficiency, and deployment costs at enterprise scale.
- Knowledge of multimodal models, emerging AI ecosystems, open\-source AI communities, agent technologies, and developer productivity tools.
- Proven track record delivering complex technology platforms at scale and driving cross\-organizational transformation initiatives across engineering, research, product, infrastructure, and business organizations.
- Ability to operate at both strategic and hands\-on levels while translating emerging AI capabilities into measurable business impact.
ACADEMIC CREDENTIALS:
- Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Machine Learning, Software Engineering, or a related technical field; Master’s, PhD degree preferred.
LOCATION: Seattle, WA, or Santa Clara, CA or Austin, TX This role is not eligible for visa sponsorship.
\#LI\-TL1
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% 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 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).
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 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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