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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:
AMD's Software and Solutions Team is seeking a Senior Agentic AI Solutions Engineer to design, build, and deliver next\-generation Agentic AI solutions built on AMD's heterogeneous computing portfolio. Working under the technical direction of AMD's Agentic AI Systems Architecture team, this role drives the hands\-on development, integration, and performance optimization of end\-to\-end AI workflows that leverage AMD CPUs, GPUs, DPUs, networking, and software platforms to deliver secure, scalable, deterministic, and high\-performance AI solutions for AMD's most strategic customers.
Working across AMD's AI software ecosystem—including AMD \+ AMD (A\+A) and AMD \+ NVIDIA (A\+N) environments—and leveraging ROCm™, AMD Zen Software Studio, AMD Enterprise AI, and AMD's open\-source software initiatives, this engineer will translate architectural vision into working customer solutions, reference implementations, and early customer proof\-of\-concepts (PoCs) across enterprise, cloud, sovereign AI, and edge deployments. Delivered solutions will focus on execution, performance, efficiency, data integrity, and security, including governance, compliance, and AI sovereignty, while providing implementation feedback that helps shape AMD silicon and software roadmaps.
This is a deeply hands\-on, customer\-facing senior individual contributor role focused on solution delivery, technical implementation, and performance engineering. It is not a people management, program management, or alliance management position.
THE PERSON:
A deep hands\-on engineer with strong expertise spanning AI infrastructure, virtualization systems, distributed systems, heterogeneous computing, intelligent data architectures, operating systems, cloud and Neo Cloud environments, networking, security architecture (including secure computing and cryptography), and large\-scale AI platform design, complemented by broad experience across modern AI software stacks, enterprise solutions, and software engineering methodologies. This individual has a demonstrated history of delivering complex customer solutions and shipping working systems that span hardware, software, networking, security, and AI frameworks.
The ideal candidate understands how frontier foundation models, agentic reasoning systems, orchestration frameworks, intelligent data structures, and heterogeneous compute resources interact to deliver scalable, deterministic, trustworthy, and production\-ready AI systems. They thrive in customer\-embedded environments where they can turn architectural vision into working code, benchmarks, and customer proof\-points leading through technical excellence, deep coding ability, and solution delivery alongside the Fellow\-level architect.
KEY RESPONSIBILITIES:
- Deliver on AMD's Agentic AI technical strategy across enterprise, cloud, sovereign AI, edge, and emerging AI deployment environments, translating architectural vision into working customer solutions, reference implementations, and PoCs.
- Build and optimize end\-to\-end reference solutions that maximize heterogeneous execution across AMD CPUs, GPUs, DPUs, networking, memory, storage, and future accelerator technologies — supporting both AMD \+ AMD (A\+A) and AMD \+ NVIDIA (A\+N) deployment models.
- Deliver intelligent multi\-agent systems and orchestration frameworks that optimize execution across heterogeneous compute resources while balancing performance, latency, throughput, power efficiency, scalability, and cost.
- Optimize Agentic AI execution end\-to\-end — task decomposition, multi\-agent collaboration, workflow scheduling, reasoning pipelines, context management, token and inference efficiency, and resource allocation — balancing performance, cost, energy, security, and quality of service across heterogeneous AI infrastructure.
- Implement innovative approaches for data optimization and intelligent data structures that improve retrieval efficiency, knowledge representation, inference performance, memory hierarchy utilization, and overall AI system scalability.
- Deliver solutions that improve deterministic AI behavior, reduce hallucinations, and increase reliability, explainability, and trustworthiness of Agentic AI systems through grounding techniques, orchestration strategies, policy enforcement, validation frameworks, and intelligent workflow design.
- Build trusted, secure AI solutions incorporating data provenance, data attestation, lineage tracking, governance, compliance, and AI sovereignty, leveraging confidential computing, trusted execution environments, hardware roots of trust, attestation, and cryptographic techniques.
- Integrate frontier foundation models with retrieval systems, enterprise applications, orchestration frameworks, edge computing, sovereign AI environments, and hybrid cloud deployments.
- Partner directly with strategic customers, hyperscalers, OEMs, ISVs, and open\-source communities to embed, deliver, and validate AMD Agentic AI solutions in production\-oriented environments, accelerating customer adoption and reference outcomes.
- Partner with silicon architecture, software engineering, AI framework, and product management teams to provide implementation\- and customer\-driven feedback that informs future AMD silicon, software, and platform roadmaps.
PREFERRED EXPERIENCE:
- Strong hands\-on experience delivering large\-scale AI infrastructure, distributed AI systems, cloud\-native AI platforms, or enterprise AI solutions across heterogeneous computing architectures spanning CPUs, GPUs, DPUs, networking, storage, memory subsystems, virtualization, and cloud infrastructure.
- Strong understanding of modern AI software ecosystems, including ROCm™, AMD Enterprise AI, AMD Zen Software Studio, frontier foundation models, LLM inference, Retrieval\-Augmented Generation (RAG), vector databases, model serving, and AI orchestration frameworks.
- Hands\-on experience delivering Agentic AI solutions — multi\-agent collaboration, orchestration frameworks, autonomous reasoning, planning, workflow optimization, and intelligent task execution.
- Deep hands\-on expertise in data optimization, intelligent data structures, knowledge representation, vector databases, and retrieval architectures supporting enterprise\-scale AI systems.
- Strong expertise in AI performance engineering — latency optimization, throughput, token efficiency, inference scalability, workload scheduling, and heterogeneous resource utilization.
- Experience delivering methodologies that improve AI reliability through deterministic execution, hallucination mitigation, grounding, explainability, and trusted AI outcomes; working knowledge of confidential computing, trusted execution environments, attestation, data provenance, and enterprise AI security.
- Deep hands\-on coding ability with strong programming skills in Python, C, C\+\+ and comfort in performance\-critical code paths, profiling, and optimization.
- Background as a solutions engineer, applications engineer, or technical consultant at a major systems or platform company (e.g., HP, Dell, or comparable) or a top\-tier consulting/integration organization is a strong plus. Excellent written and verbal communication; comfort embedding with customer engineering teams and presenting to senior customer stakeholders.
ACADEMIC CREDENTIALS:
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Data Science, or related field preferred; advanced degree desired.
ALTERNATE LOCATION:
- Santa Clara, CA
This role is not eligible for visa sponsorship.
\#LI\-TB1
\#LI\-Hybrid
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
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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