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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
We have an exciting new opportunity available due to growth for an Agentic AI Software Application Developer to architect how our team connects to data, exposes capabilities, and delivers high\-quality AI outcomes. This is a systems thinking and judgment role — each project is evaluated independently, the right data access strategy is determined from first principles, and the capability layer is built to make AI tools genuinely useful to the people who rely on them.
Our team is a highly technical integrated business function, not a software engineering team, and the core of this role is implementing and maintaining AI capability interfaces — MCP\-based servers, callable skills, and API endpoints — that connect AI agents to enterprise business systems with the security, governance, and access controls those environments require. AI output quality is owned end to end: testing, evaluating, monitoring, and continuously improving what AI delivers to users.
This role owns both strategy and execution — determining the right approach for each project, designing the capability layer that defines what AI can do for our team, and directly shaping the quality of the intelligence our colleagues rely on every day.
This role can be remote based within the United States.
THE PERSON
You are a systems thinker, seeing flows, access patterns, and failure modes before writing code. You are known for your business process awareness, designing for context and meaning, not just technical correctness; as well as being known for your sound judgment on data access strategy, able to assess a project, weigh the tradeoffs, and make a defensible architectural call.
Your security\-first mindset is the basis for your success in creating governed, auditable, permission\-respecting access as a baseline for any AI capability, development and output evaluation. This is a part of your genuine orientation toward output quality, and knowing that what the AI says to the user matters as much as whether the system ran.
You have the ability and a good comfort level for operating in ambiguity as the field continues to evolve rapidly.
KEY RESPONSIBILITIES
Capability Design \& AI Interface Architecture
- Design the interface layer connecting AI tools and agents to business systems, selecting the right access pattern for each project
- Make clear, reasoned decisions about live retrieval vs. caching vs. staging vs. structured storage — and own those decisions
- Implement and maintain MCP\-based capability interfaces and callable skills with explicit inputs, outputs, and scopes
- Design clean, typed request/response contracts optimized for how AI agents reason and respond
Security, Governance \& Access Control
- Design governed access to business systems using RBAC, SSO, and appropriate permission frameworks
- Ensure every capability interface respects the security model of the underlying system — authentication, authorization, audit logging, and rate limiting are requirements, not afterthoughts
- Evaluate data sensitivity and access requirements before connecting any new system to the AI capability layer
Enterprise Systems \& Data Integration
- Connect AI capabilities to enterprise business platforms — including analytics tools, collaboration systems, graph APIs, relational databases, and other organizational data sources
- Write Python and SQL to query, transform, and shape data from enterprise sources for AI consumption
- Understand the business processes behind the data — design for business context, not just schema
- Define and consume REST and GraphQL APIs; write OpenAPI specs and JSON Schema definitions
AI Output Quality \& Evaluation
- Design and run evaluation frameworks measuring accuracy, relevance, groundedness, and hallucination rates
- Diagnose root causes when AI tools produce poor or inaccurate responses, tracing failures to interface design, retrieval strategy, or data quality
- Establish quality standards across every capability the team builds and continuously improve based on observed outcomes
Documentation, Patterns \& Enablement
- Build production\-quality documentation for every capability, endpoint, and integration
- Develop reusable patterns and frameworks that make building new capabilities faster and more consistent
- Train team members on AI\-friendly system design, capability decomposition, and security considerations
- Stay current with the evolving AI tooling and agent ecosystem and bring relevant advancements back to the team
PREFERRED QUALIFICATIONS
Highly preferred:
- Understanding and experience in systems design, API development, platform engineering, or solutions architecture
- Experience integrating with enterprise business platforms such as analytics tools, collaboration systems, and relational databases
- Working knowledge of RBAC, SSO, and governed access control in enterprise environments
- Python and SQL proficiency — comfortable querying, transforming, and integrating data from enterprise systems
- Hands\-on experience designing and documenting APIs (OpenAPI, JSON Schema, REST, GraphQL) in production
- Active daily use of modern AI developer tools as a builder — using these tools to build things, not just ask questions
- Demonstrated ability to decompose business processes into discrete, callable capabilities with clear contracts
- Track record of shipping production\-grade systems, not just proofs of concept
Preferred:
- Experience implementing or configuring MCP servers or AI agent tool/skill definitions
- Experience designing AI evaluation frameworks (hallucination, relevance, groundedness)
- Background in semiconductor, hardware, or high\-tech manufacturing — familiarity with engineering data, supply chain, PLM, or EDA systems is a plus
- Familiarity with RAG architectures and how retrieval design affects AI output quality
- Experience in a business\-embedded, non\-IT team making independent integration and architecture decisions
ACADEMIC CREDENTIALS
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field
This role is not eligible for visa sponsorship.
\#LI\-DNI
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. Mid-level AI roles across all categories have a median of $200,000.
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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