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About This Role
Overview:
ADVANCE YOUR CAREER. ADVANCE THE WORLD.
At AMD, we believe technology can change lives for the better. It can heal us, entertain us, and make us more connected, productive, and understanding of the world around us. And we’re looking for talent who feel the same: people who want to leave the planet better than they found it, those who don’t shy away from humanity’s challenges but are determined to help solve them.
AMD is powering the next generation of supercomputing, high\-performance computing, cloud, and AI. Whether you’re designing next\-gen processors, enabling AI breakthroughs, or creating go\-to\-market plans, every role at AMD contributes to something bigger — technology that moves the world forward.
Responsibilities:
THE ROLE
This role focuses on leading system\-level, rack\-level, and cluster\-scale validation for next\-generation AI infrastructure and accelerated computing platforms. You will define validation strategies, drive technical direction, and lead complex validation initiatives spanning hardware, firmware, software, networking, and infrastructure domains.
You will develop and enhance automated validation frameworks, telemetry\-driven analysis pipelines, and large\-scale test infrastructure used to qualify AMD AI platforms from initial bring\-up through customer deployment readiness. The role requires deep technical expertise in hardware\-software interactions, strong analytical and debugging skills, and the ability to influence technical decisions across multiple engineering organizations.
Working closely with architecture, silicon, platform engineering, firmware, software, manufacturing, and deployment teams, you will identify quality risks, drive root\-cause analysis, establish scalable validation methodologies, and ensure product quality, reliability, and deployment readiness at scale.
THE PERSON
You are a technical leader with extensive experience validating complex server, rack\-scale, or distributed computing environments. You thrive on solving difficult technical problems, building scalable validation approaches, and driving improvements that increase platform quality and engineering efficiency.
You are comfortable operating with minimal supervision while providing technical leadership across engineering teams. You possess strong debugging skills, sound technical judgment, and a deep understanding of hardware\-software interactions across modern server platforms.
You proactively identify technical risks, establish effective validation strategies, and drive resolution of complex platform issues. You are passionate about mentoring engineers, improving validation methodologies, and influencing engineering decisions that improve product quality, validation effectiveness, and customer readiness.
KEY RESPONSIBILITIES
- Lead system\-level, rack\-level, and cluster\-scale validation strategy for AI server and accelerated computing platforms.
- Define and drive comprehensive validation plans that represent real\-world customer deployment scenarios across scale\-up and scale\-out infrastructures.
- Design, develop, and maintain automated validation frameworks, orchestration systems, telemetry pipelines, and data\-driven workflows used to qualify AI platforms from initial bring\-up through platform lifecycle.
- Develop validation methodologies spanning CPUs, GPUs, memory subsystems, PCIe fabrics, platform firmware, BIOS/UEFI, BMCs, networking, storage, operating systems, rack management infrastructure, and cluster\-level software components.
- Execute and analyze large\-scale validation workloads across lab, manufacturing, and pre\-production environments to evaluate platform reliability, stability, scalability, performance, and operational readiness.
- Lead investigation and root\-cause analysis of complex hardware, firmware, software, networking, and infrastructure issues using structured debugging methodologies and telemetry\-driven analysis.
- Develop tooling and automation to collect, aggregate, and analyze health and performance telemetry from technologies such as Redfish, IPMI, OpenBMC, PCIe AER, SEL/SDR logs, operating system diagnostics, GPU telemetry, and platform management interfaces.
- Drive technical decision\-making within the validation organization and influence cross\-functional teams to resolve product quality risks and readiness concerns.
- Establish validation readiness criteria, release qualification requirements, coverage metrics, and quality gates that improve execution consistency and early risk identification.
- Partner closely with architecture, silicon, firmware, software, systems engineering, manufacturing, deployment, and support organizations to continually improve product quality, reliability, and validation effectiveness.
- Own and evolve validation content, test procedures, troubleshooting guides, release documentation, and engineering standards used to support repeatable execution across validation environments.
- Work directly with technicians, validation engineers, and operational teams executing your test content, providing technical guidance, reviewing findings, driving procedural consistency, and improving test development practices across programs.
- Mentor engineers and provide technical leadership that improves debugging effectiveness, automation capabilities, validation methodologies, and overall engineering excellence.
- Communicate validation status, readiness assessments, technical recommendations, quality metrics, and key risks to engineering leadership and stakeholders.
- Drive continuous improvement of validation frameworks, automation infrastructure, tooling, documentation, and development processes to increase organizational efficiency, test coverage, and product quality.
PREFERRED EXPERIENCE
- Experience validating large\-scale AI, cloud, HPC, server, rack\-scale, or data center platforms.
- Strong background in hardware\-software integration, platform validation, system bring\-up, infrastructure qualification, and deployment readiness activities.
- Experience developing software and automation using Python, Linux, and modern test orchestration frameworks.
- Proven ability to investigate and resolve complex issues spanning hardware, firmware, operating systems, networking, storage, platform management, and distributed infrastructure environments.
- Experience leveraging telemetry, observability, and large\-scale data analysis to improve validation coverage, root\-cause analysis, and engineering decision\-making.
- Familiarity with server and rack technologies including:
- + AMD EPYC processors and Instinct accelerators
+ GPU management and observability frameworks
+ PCIe architecture and AER diagnostics
+ BIOS/UEFI and firmware validation
+ BMC technologies, IPMI, Redfish, and OpenBMC
+ InfiniBand, Ethernet, RoCE, and scale\-up interconnects
+ Storage technologies and NVMe validation
+ Cluster management and distributed computing environments
- Experience supporting manufacturing validation, ODM/OEM integration, deployment qualification, customer readiness, or hyperscale infrastructure programs.
- Demonstrated ability to lead cross\-functional technical initiatives and influence engineering decisions across organizational boundaries.
- Strong verbal and written communication skills with the ability to communicate technical findings, quality risks, recommendations, and readiness assessments to engineers, technicians, program managers, and senior leadership.
ACADEMIC CREDENTIALS
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or a related technical discipline.
LOCATION
- Secaucus, New Jersey (short commute from NYC)
- This role is expected to be primarily onsite with flexibility in accordance with AMD workplace guidelines.
- Regular hands\-on interaction with servers, AI racks, cluster infrastructure, validation laboratories, and deployment environments is required.
- Periodic support during critical platform bring\-up activities, release qualification milestones, customer readiness events, and program ramp cycles may be required.
This role is not eligible for visa sponsorship.\#LI\-KW1
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 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 $218,750 based on 3,817 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/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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