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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 We are seeking a Lead HPC & AI Infrastructure Architect to own the architecture and technical direction of large-scale CPU and GPU platforms across on-premises infrastructure, public cloud, and specialized GPU cloud providers. You will translate scientific computing, engineering, and AI requirements into secure, reliable, and cost-effective platforms for tightly coupled HPC workloads, distributed model training, and inference services. This hands-on leadership role spans design, workload validation, deployment, and lifecycle management. You will guide infrastructure engineering and SRE teams and partner with researchers, AI engineers, cloud providers, and hardware vendors to deliver production-ready platforms. THE PERSON You combine deep systems expertise with practical architectural judgment. You take ownership of complex technical problems, validate decisions with measurable results, and communicate tradeoffs clearly. You collaborate across engineering, operations, security, and research teams; mentor engineers; and balance performance, reliability, security, delivery timelines, and cost. You can independently guide initiatives from requirements through production acceptance. KEY RESPONSIBILITIES* Own the HPC and AI infrastructure roadmap, reference architectures, and standards for CPU/GPU clusters. Define workload placement across bare metal, virtual machines, Slurm, and Kubernetes.
- Lead designs covering CPU/NUMA and GPU topology, accelerator interconnects, networking, storage, and identity. Establish validated hardware, firmware, driver, and runtime baselines.
- Architect RDMA-capable fabrics and storage tiers. Validate collective communication, parallel I/O, dataset delivery, and checkpoint performance at target scale.
- Design resource sharing through Slurm accounting, fair-share, quotas, QoS, preemption, and tenant isolation. Enable reproducible environments, self-service onboarding, and inference capacity planning.
- Standardize provisioning, configuration, and fleet upgrades through infrastructure as code, automation, and CI/CD; manage images, containers, and configuration drift.
- Define production acceptance criteria, service-level objectives, and fleet observability. Partner with SRE on fault isolation, job recovery, checkpoint/restart, incident analysis, and capacity planning.
- Benchmark HPC and distributed AI workloads; improve scaling efficiency, throughput, inference latency, GPU utilization, and cost efficiency through bottleneck analysis.
- Embed federated identity, least-privilege access, tenant and network isolation, secrets management, controlled container privileges, audit logging, and vulnerability management.
- Lead architecture reviews, proofs of concept, provider evaluations, and technical escalations. Document tradeoffs, mentor engineers, and align delivery with stakeholders and infrastructure partners. PREFERRED EXPERIENCE* Typically 10+ years in infrastructure, cloud engineering, or distributed systems, including 5+ years designing and delivering production HPC or GPU platforms; equivalent demonstrated experience is welcome.
- Ownership of large-scale cluster deployments involving hundreds of nodes or comparable complexity, with measurable improvements in workload performance, reliability, or cost.
- Deep expertise in a major cloud platform such as Azure, OCI, or AWS, with experience integrating cloud and on-premises systems. Familiarity with specialized GPU providers such as Crusoe or TensorWave.
- Strong Linux troubleshooting skills across CPU/NUMA topology, PCIe, GPU connectivity, drivers, virtualization, containers, and distributed runtime environments.
- Hands-on Slurm scheduling and resource governance, plus Kubernetes experience. Understanding of MPI, distributed training, collective communication, and AI inference infrastructure.
- AMD Instinct GPUs, ROCm, RCCL, and multi-node PyTorch workloads, including compatibility validation and performance troubleshooting.
- HPC networking and storage, including RDMA, RoCE or InfiniBand, topology and oversubscription tradeoffs, parallel I/O, data placement, checkpointing, and enterprise connectivity.
- Parallel and shared storage technologies such as Lustre, IBM Storage Scale, BeeGFS, NFS, or VAST, and object storage; network, storage, and collective benchmarks for cluster acceptance.
- Terraform or equivalent infrastructure as code, Ansible or comparable configuration management, Python or Bash, and Git-based CI/CD. Familiarity with Azure CycleCloud or AWS ParallelCluster.
- Observability and SRE practices using tools such as Prometheus and Grafana, including secure access, resource controls, fault recovery, and production readiness.
- Experience establishing observability and SRE practices using tools such as Prometheus and Grafana, and designing security boundaries and resource controls for shared infrastructure.
- Ability to lead cross-functional technical work independently, communicate architecture tradeoffs clearly, and guide projects from requirements through production acceptance.
- Coordination with hardware and data center teams on rack power, liquid cooling, facility capacity, and other constraints affecting GPU platform design.
Preferred qualifications
- Hands-on experience with AMD Instinct GPUs, ROCm, RCCL, and multi-node PyTorch workloads, including compatibility validation and performance troubleshooting.
- Experience with Azure, OCI, Crusoe, or TensorWave GPU environments, and cluster provisioning frameworks such as Azure CycleCloud or AWS ParallelCluster.
- Experience with Lustre, IBM Storage Scale, BeeGFS, or VAST, plus network, storage, and collective communication benchmarks used for cluster acceptance.
- Experience with LLM serving platforms such as vLLM or SGLang, model parallelism, inference capacity planning, and usage accounting or cost allocation.
- Understanding of rack power, liquid cooling, and facility capacity constraints, with experience coordinating platform requirements with hardware and data center teams.
ACADEMIC CREDENTIALS Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience. This role is not eligible for visa sponsorship.
#LI-RD1 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
AMD AI Hiring
AMD has 19 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, LLM Engineer, AI Product Manager. Positions span San Jose, CA, San Jose, CA, US, Santa Clara, CA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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