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About This Role
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:
This is a hands\-on role for engineers who thrive on exploration, love solving complex systems problems, and are passionate about HPC and AI. You’ll bring your HPC expertise to a research and development\-focused team that investigates AI infrastructure across compute, storage, networking, and orchestration layers. Your work and knowledge will help shape reference architectures, configuration guides, and reproducible experiments that support internal teams, pre\-sales engineers, and customers in making informed hardware and software decisions. The primary focus for this role is on storage solutions for AI/HPC, benchmarking proof points, and creation of reference architecture and other supporting collateral related specifically to storage solutions in support of AMD\-based AI and HPC clustered systems at scale.
Our team operates across industry verticals as subject matter experts in the AI stack and across the cluster. We’re building a library of technical artifacts such as design docs, presentations, and “how it works” guides to help others skill up from an HPC perspective in the AI space. This is a high\-autonomy role focused on creation, not operations. If you enjoy building, learning, debugging tough issues, and writing about what you discover, we want to hear from you!
THE PERSON:
You’re an engineer, a systems thinker and professional troubleshooter who sees the big picture and thrives on researching and experimentation. You’ve worked hands\-on with HPC clusters and HPC/AI\-oriented storage solutions and are eager to explore how AI workloads like inferencing and training fit into HPC style infrastructure. You’re not looking for a runbook, you’re looking to build the blueprint.
You’re self\-directed, proactive, and comfortable navigating ambiguity to solve complex problems. You communicate clearly, enjoy writing technical artifacts that help others understand intricate systems, and collaborate naturally with internal teams and customers. You get excited to teach others what you know. Whether you’re diving into a new stack or refining a reference architecture, you bring curiosity, initiative, and a drive to create.
KEY RESPONSIBILITIES:* Apply your HPC expertise to shape AI infrastructure by creating reference architectures, configuration guides, and deployment blueprints that help internal teams and customers make informed hardware and software decisions
- Build a library of technical artifacts—including presentations, design documents, and “how it works” guides, to support pre\-sales engineers and enable others to skill up from an HPC perspective
- Perform deep technical evaluations of HPC and AI stacks with a specific focus on storage solutions, documenting how they work, where they fit, and the tradeoffs involved between storage technologies and vendors
- Design and execute reproducible experiments and benchmarking harnesses to compare storage technologies and their fit\-for\-purpose across AI and HPC workloads
- Develop small reference implementations and tools to validate performance hypotheses, analyze system behavior and more
- Present findings through demos, documentation, and internal talks, and create templates and checklists to support repeatable evaluations and cluster designs
PREFERRED EXPERIENCE:* Engineering mindset: Evidence of end\-to\-end systems thinking, debugging, and tradeoff decisions
- Storage/data: parallel filesystems (Lustre, BeeGFS), object stores, RDMA, data pipeline throughput and caching strategies
- Extensive knowledge of the current storage vendor landscape
- AI/HPC cluster background: hands\-on familiarity with schedulers and/or orchestration systems (e.g., Slurm, Kubernetes), MPI/OpenMP, distributed storage patterns, or performance analysis
- Comparative analysis: experience writing evaluation docs/RFCs with clear criteria, benchmarks, risks, and recommendations
- Strong Linux fundamentals: Linux operating systems, networking, filesystems, containers, performance tooling (perf, flamegraphs, nvprof/rocprof, basic eBPF)
- Clear communication: ability to turn complex systems into accessible, structured documentation with diagrams and reproducible steps
- AMD ecosystem experience: ROCm, RCCL, Instinct GPUs, EPYC platforms, compiler/toolchain impacts, and performance tuning
- Distributed training internals: DDP, collective comms, sharded/stateful optimizers; NCCL/RCCL behavior and transport considerations (PCIe, NVLink, IF)
- Orchestration models: Slurm configuration patterns, Kubernetes for HPC/AI (GPU operators, device plugins), Apptainer/Singularity
- Enterprise storage solutions (NAS, NFS), particularly large scale, and design patterns for federation/replication, backup, and DR
- IaC literacy: Terraform/Ansible for reproducible blueprints—focused on design and sample configs, not running prod clusters
- Documentation tooling: reproducible docs/workbooks, literate programming notebooks, CI for benchmarks
ACADEMIC CREDENTIALS:* Bachelors or Masters degree in electrical, computer, or related engineering field
LOCATION:
Austin, TX (preferred), Santa Clara, Ca., Secaucus, NJ., and Markham, Canada This role is not eligible for visa sponsorship. \#LI\-CB1
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.*
Salary Context
This $163K-$244K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($204K) sits 7% below the category median. Disclosed range: $163K to $244K.
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 Austin pay a median of $214,343 across 87 tracked positions.
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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