Interested in this AI/ML Engineer role at AMD?
Apply Now →Skills & Technologies
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 TEAM:
We are a small, AI-forward data science team with two jobs. We pull commercial data out of Salesforce through MCP into Snowflake and turn it into AI dashboards built for the question each audience is actually asking, and we own the shared library where AMD teams publish the AI skills and agents they build. The dashboards are where we prove AI-assisted analysis holds up against real, high-stakes data; the library is how that work stops being ours alone.THE ROLE:
AMD is seeking a Principal Program Manager to own the operating model across both halves of that charter—the standards, decision rights, cadence, and measurement that turn scattered good work into a system. You will own the dashboard portfolio as a product rather than a request queue: who each surface is for, what decision it drives, what gets built next, and what gets retired. Co-equal with that, you will own the trust layer underneath—definitions, named owners, pipeline reliability, and the honest answer when a number is wrong. On the library side, you will define how reusable AI assets are contributed, reviewed, and published, charter the councils that decide, and own the promote-to-enterprise path to peer AI Libraries across AMD. This is a senior individual contributor role with a builder bench alongside you, not underneath you. Hands-on skill—SQL, Python, Snowflake, dbt, dashboards, or agent engineering—is welcome and will make you more effective. It is not a requirement.THE PERSON:
You have moved an organization from AI enthusiasm to AI operating behavior, and you know the difference between deployment and adoption. You are fluent enough in how skills, agents, and prompts are built to set a quality bar engineers respect, and you would rather partner with builders than out-argue them. You bring editorial judgment to a domain where it is trivial to produce more dashboards and more assets than anyone will use. KEY RESPONSIBILITIES:
AI Dashboards & Decision Products
- Own the dashboard portfolio as a product: audience, decision it drives, roadmap, and what gets retired.
- Differentiate the IC, manager, and executive views—each answering a different question, not one dataset shown three ways.
- Define where AI belongs in a dashboard—summarization, anomaly surfacing, natural-language query—and where it does not.
- Write the requirements builders implement: metrics, grain, filters, refresh, and acceptance criteria.
Data Foundation & Trust
- Own metric definitions and the named owner behind each one, so two dashboards cannot disagree without someone accountable.
- Set the quality bar for the Salesforce-to-Snowflake path, and partner with builders and IT on pipeline monitoring and validation.
- Run the triage path when a number is disputed: find the truth, publish the correction, and fix the cause upstream.
AI Library & Review Councils
- Define and operate the asset lifecycle: submission, review, quality bar, versioning, publication, and retirement.
- Charter and run the councils—membership, intake, pre-read, decision rights, escalation—and build the criteria-based go/no-go framework they decide against.
- Own the promote-to-enterprise path, including the security, privacy, and Responsible AI review gate.
Adoption & Measurement
- Design the adoption architecture: phased onboarding, rollout sequencing, champion engagement, and support model.
- Measure adoption on consistent usage, active cohorts, and value proxies—not license counts—and report it on a shared scorecard, including what is not working.
PREFERRED EXPERIENCE:
- Program, transformation, or operations leadership at enterprise scale, turning executive direction into governance, cadence, and measurable outcomes.
- Ownership of an AI or digital adoption program: rollout sequencing, champion networks, readiness criteria, adoption measurement.
- Experience designing operating models—decision rights, intake and approval workflows, escalation, executive review cadence.
- Ownership of a reporting portfolio serving ICs, managers, and executives, including the discipline to retire what stopped earning its place.
- Working knowledge of Salesforce commercial data and a modern cloud warehouse, and of what breaks between source system and dashboard.
- Fluency with generative AI—LLMs, agents, prompting, evaluation—enough to set a credible quality bar with technical contributors.
- Strong executive communication, influence without authority, and semiconductor or comparable industry experience are all a plus.
Recommended, not required. Hands-on depth in SQL, Python, Snowflake or dbt, dashboard development, MCP and API integration, or agent engineering is weighted favorably but is not a screening criterion.ACADEMIC CREDENTIALS:
- Bachelor's or Master's degree in Engineering, Information Systems, Business, or a related field, or equivalent experience.
LOCATION: Austin, TX
This role is not eligible for visa sponsorship.
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 Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.