AMD is actively hiring for 28 AI and machine learning positions across AI/ML Engineer (21), AI Software Engineer (5), and Research Engineer (1) roles. Posted salary ranges span $246K - $246K, though only 3% of listings disclose compensation. The median posted ceiling sits at $246K. Positions are based in San Jose, CA, US, Austin, TX, US, Santa Clara, CA, US. The most frequently requested skills across these postings are Python, Pytorch, Tensorflow, Jax, Azure. Mid-level roles account for 53% of openings.
Skills & Technologies
Locations
San Jose, CA, US, Austin, TX, US, Santa Clara, CA, US, Secaucus, NJ, US
Hiring by Role Category
Open Positions (showing 25 of 28)
Sr. Software Engineer - AI Triton Kernels
Sr. Software Engineer - AI Triton Communication
Embedded Software Engineer - Physical AI
AI/HPC Data Center Rack Design Engr
Principal Software Quality Engineer – GPU & Machine Learning
Systems Design Engineer (AI, Software)
AI Networking System/Solution Test Engineer
Director, AI Research
Director, AI Research - Strategic Partnerships
Director, AI Research - Recursive Self-Improvement
AI/ML Silicon Verification Solutions Engineer – Tools
Senior ASIC Package Design Automation and AI Engineer
Fellow Software Engineer — AI Performance & Reliability
Thermal Engineer-Data Center AI GPU Thermal Design
Senior Manager, AI Power & Performance Engineering
Lead Software Systems Engineer – AI & Automation
Director, AI Systems – Scale-up, Scale-out and Networking
AI Business Planning Specialist
ML Systems Research Engineer, RL / Inference / Agent Systems
Data Center GPU AI Customer Debug Lead
Lead AI Validation and Test Engineer
Princiapl AI Validation and Test Engineer
System Design Engineer - AI Cluster Software Engineer
AI Validation and Test Engineer
What AMD's hiring tells you
With 28 active AI roles spanning 4 role types, hiring at this scale signals AI is core to the business model, not a pilot. Companies in this tier typically have a named AI leader (VP AI, Head of ML), dedicated infrastructure budget, and a multi-year roadmap. Posted compensation range ($246K - $246K) suggests transparent and competitive pay practices.
The skill mix here leans toward Python in AI Software Engineer roles. That is a clue about what AMD is building: teams hire for the work in front of them, not the work they wish they were doing.
Questions worth asking in the AMD interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- How is the AI org structured, and who does it report to (CTO, CEO, separate AI leader)?
- What was the most recent ML system that shipped to production, and what was the scope?
- How much of compute spend is on inference vs training, and how is that decided?
AMD AI and ML Hiring
AMD has 28 active AI and ML roles in our dataset. Open positions span AI Software Engineer, AI/ML Engineer, Research Engineer, AI Product Manager. Compensation ranges from $246K - $246K across disclosed roles. Roles are based in San Jose, CA, US, Austin, TX, US, Santa Clara, CA, US, Secaucus, NJ, US.
Salary Benchmarks
The market median for AI roles is $215,000. AI Software Engineer roles pay a median of $220,400 across the market. AI/ML Engineer roles pay a median of $215,000 across the market. Research Engineer roles pay a median of $272,100 across the market. Top-quartile AI compensation starts at $267,900.
Skills AMD Looks For
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
AI Role Categories
AI Software Engineer
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Market compensation for AI Software Engineer roles: $220,400 median across 623 positions with disclosed pay.
AI/ML Engineer
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.
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.
Market compensation for AI/ML Engineer roles: $215,000 median across 5,661 positions with disclosed pay.
Research Engineer
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Market compensation for Research Engineer roles: $272,100 median across 204 positions with disclosed pay.
AI Product Manager
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
Market compensation for AI Product Manager roles: $218,550 median across 418 positions with disclosed pay.
The AI Job Market Today
The AI job market spans 4,109 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,893), Data Scientist (308), AI Software Engineer (293). 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 (120) are outnumbered by mid-level (1,975) and senior (1,609) 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 405 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (642 positions), with 3,444 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 $215,000. Top-quartile roles start at $267,900, and the 90th percentile reaches $322,980. 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 $275,000 median, while AI Consultant roles sit at $148,848. 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 (2,102 postings), Aws (1,190 postings), Azure (917 postings), Rag (897 postings), Gcp (673 postings), Prompt Engineering (582 postings), Pytorch (581 postings), Kubernetes (538 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.
AI Hiring Overview
The AI job market has 4,109 open positions tracked in our dataset. By seniority: 120 entry-level, 1,975 mid-level, 1,609 senior, and 405 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (642 positions). The remaining 3,444 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $267,900. The 90th percentile reaches $322,980. Highest-paying categories: AI Safety ($275,000 median, 33 roles); Research Engineer ($272,100 median, 204 roles); AI Engineering Manager ($250,000 median, 19 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Frequently Asked Questions
Frequently Asked Questions
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