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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: ABOUT THE DEPARTMENT: Central DFX (CDFX) is a centralized ASIC design organization within AMD Technology and Engineering, with teams across North America and Asia. CDFX develops and standardizes Design-for-Test (DFT) and Design-for-Debug (DFD) architectures, methodologies, implementation flows and CAD automation for high-performance and adaptive-compute products serving data center, embedded, gaming and PC markets.THE ROLE: We are seeking an AI/ML Project Lead who combines deep DFT design and methodology expertise with hands-on experience developing production-quality AI solutions and agentic engineering workflows. This technical leadership role will define and drive a portfolio of AI-enabled capabilities that improve the productivity, quality, scalability and predictability of DFX design and verification flows.THE PERSON: You will partner with DFT, DFD, CAD, design, verification and software engineering teams to identify high-value use cases, translate engineering knowledge into deployable solutions, and lead projects from concept and prototype through production deployment, adoption and sustained operation. The role requires strong technical judgment across semiconductor design, AI/ML, software architecture and program execution. KEY RESPONSIBILITIES:* Lead the strategy, roadmap and execution of AI/ML and agentic AI initiatives for DFT and DFD design, methodology, verification and debug workflows.
- Integrate AI agents and services with DFX design flows, EDA tools, repositories, specifications, regression systems and engineering data sources through secure, maintainable interfaces.
- Develop AI-enabled expert systems, predictive models and intelligent automation to address DFT challenges across IP, subsystem and SoC design environments.
- Maintain awareness of advancements in AI/ML, agentic systems and semiconductor design automation, and evaluate their relevance to AMD DFX needs.
- Identify and prioritize use cases based on measurable engineering value, technical feasibility, data readiness, deployment risk and opportunities for reuse across teams.
- Architect agentic workflows that combine large language models, domain knowledge, retrieval, tools, automation and human-in-the-loop controls to support complex engineering tasks.
- Establish evaluation methods, quality gates, guardrails, traceability and fallback mechanisms appropriate for production engineering workflows.
- Lead model and application deployment, monitoring, incident response, maintenance and continuous improvement using sound MLOps, DevOps and software lifecycle practices.
- Drive scalable DFT implementation and verification automation using Python, Perl, Tcl and shell scripting within complex ASIC design environments.
- Coordinate cross-functional project plans, dependencies, risks, milestones and technical decisions, and communicate progress clearly to engineering and leadership stakeholders.
- Mentor engineers, promote reusable architecture and coding practices, and build organizational capability in responsible AI-assisted engineering.
REQUIRED SKILLS AND EXPERIENCE:* Extensive experience in DFT and/or DFD architecture, design, implementation, verification or methodology for high-performance SoCs and IP.
- Demonstrated technical leadership of complex, cross-team engineering initiatives from definition through production adoption.
- Hands-on experience developing and deploying AI/ML applications in production or enterprise engineering environments.
- Familiar with agentic AI systems design, including tool use, multi-agent/subagent systems, Model Context Protocol (MCP), workflow orchestration, Retrieval-Augmented Generation (RAG), structured outputs, evaluation and human oversight.
- Experience designing agentic AI systems, including tool use, workflow orchestration, retrieval-augmented generation, structured outputs, evaluation and human oversight.
- Strong Python development skills and experience with relevant AI/ML frameworks and libraries. Familiarity with scikit-learn, PyTorch, TensorFlow or equivalent technologies is desirable.
- Experience with software engineering and deployment practices such as source control, code review, automated testing, CI/CD, APIs, containers and observability.
- Knowledge of MLOps and data lifecycle practices, including data quality, model evaluation, versioning, monitoring and reproducibility.
- Proficiency with one or more ASIC/EDA automation languages, including Perl, Tcl and shell scripting.
- Strong foundation in digital logic, VLSI design and the interactions among test, power, timing, area, physical design and verification constraints.
- Proven ability to influence technical direction across organizational boundaries and convert ambiguous problems into executable project plans.
PREFERRED QUALIFICATIONS:* Experience integrating AI capabilities with EDA tools, semiconductor design flows or large-scale engineering automation environments.
- Experience with knowledge representation, engineering specifications, graph-based data, vector search or domain-specific retrieval systems.
- Familiarity with secure AI deployment, responsible AI practices, access controls, intellectual-property protection and auditability.
- Python development skills and hands on development experience with relevant AI/ML frameworks and libraries such as Claude SDK / OpenAI SDK.
ACADEMIC CREDENTIALS:* BSEE, MSEE, Computer Engineering, Computer Science or an equivalent technical degree preferred.
This role is not eligible for visa sponsorship.
#LI-MR1 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
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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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