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About This Role
Our vision is to transform how the world uses information to enrich life for all.
Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.
Job Summary – Member of Technical Staff - Agentic AI Architect, Smart Manufacturing AI Pathfinding & Innovation (AIPI)
Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The memory solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing.
The Smart Manufacturing and AI team at Micron Technology is looking for an ambitious engineer (Member of Technical Staff | MTS) to deliver industry-winning Agentic AI solutions that power Micron’s dominance in the highly competitive memory solutions market. This role sits at the intersection of applied Agentic AI engineering and production systems work: you will architect autonomous AI Agents and the agentic and data pipelines that power them, build robust CI/CD deployment paths to Kubernetes and containers, and push the limits of agent performance profiling and evaluation to turn massive data and compute into measurable manufacturing impact. Qualified applicants will have experience across data/cloud technologies and systems engineering, with extensive practice modeling data, querying, and deploying scalable pipelines to execute AI agents. You will collaborate with Data Scientists, Data Engineers and expert users to build and deploy scalable Agentic AI solutions that drive value and insight from Micron’s manufacturing processes and systems.
If you enjoy solving hard problems at scale, thrive on turning speculative research into production-grade systems, and want to see your work drive silicon to market, this is the team for you.
Responsibilities include, but not limited to:
- Design and develop autonomous AI Agents capable of multi-step reasoning, planning, and tool execution to automate complex manufacturing workflows.
- Implement Agentic frameworks to orchestrate LLM interactions with internal APIs, databases, and software tools.
- Build and maintain agent evaluation harnesses to benchmark agent accuracy, reliability, and task success across complex, multi-step workflows.
- Analyze and profile agent and inference workloads (e.g., tool-calling latency, multi-agent orchestration) to identify and resolve bottlenecks in compute and latency, using agent observability and profiling tools (e.g., PyTorch Profiler, LangSmith, OpenTelemetry).
- Implement interoperable multi-agent systems using the Agent-to-Agent (A2A) protocol and the Model Context Protocol (MCP), including MCP tools, plugins, and skills.
- Create/maintain CI/CD pipelines for deploying AI Agents to Kubernetes and containerized environments in the cloud.
- Design and implement security controls for autonomous AI Agents, including sandboxing, access control, and mitigation of prompt-injection and tool-misuse risks.
- Mentor engineers on agentic system design paradigms and optimization techniques.
Education Qualifications:
Technical Degree required (Bachelor’s or Master’s in Computer Science, Statistics, or a related field). Ph.D. is a plus.
Minimum Qualifications:
- Experience optimizing inference engines (vLLM, TensorRT-LLM) for autonomous AI Agent and LLM workloads.
- Experience developing GenAI applications and AI Agents using one or more frameworks. Microsoft Agent Framework or Google Agent Development Kit preferred.
- Proficiency with Large Language Models (LLMs), including prompt engineering, function calling/tool use, and Chain-of-Thought (CoT) reasoning.
- Experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embeddings, and vector search.
- Experience building and operating end-to-end systems that automate the testing, evaluation, and deployment of autonomous AI Agents.
- Familiarity with MCP (Model Context Protocol) tools, plugins, and skills, and the Agent-to-Agent (A2A) protocol for building interoperable multi-agent systems.
- Understanding of AI Agent security practices, including sandboxing, access control, and mitigating prompt-injection and tool-misuse risks.
- Familiarity with machine learning frameworks (PyTorch, TensorFlow, scikit-learn, etc).
- Strong scripting and programming skills, with software development skills and a desire to work in a cloud environment.
- 8+ years of relevant industry experience, spanning large-scale data/ML pipelines and/or production Agentic AI systems.
- Outstanding analytical thinking, interpersonal, oral, and written communication skills.
- Ability to prioritize and meet critical project timelines in a fast-paced environment.
- Experience with Kubernetes for orchestrating containerized applications, including deployment and scaling of AI Agent workloads.
Preferred Qualifications:
- Experience designing and coordinating Multi-Agent Systems, including collaboration between specialized agents.
- Experience designing and operating agent swarms for distributed, parallelized task execution.
- Experience with agent profiling and evaluation tooling to identify performance regressions and bottlenecks across agent pipelines.
- Hands-on experience with LangChain, LangGraph, or CrewAI.
- Proficiency in TypeScript and/or Rust.
- Demonstrated ability to study and transform data science prototypes into production solutions.
- Knowledge of computer vision and/or signal processing, including techniques for classification and feature extraction.
As a world leader in the semiconductor industry, Micron is dedicated to your personal wellbeing and professional growth. Micron benefits are designed to help you stay well, provide peace of mind and help you prepare for the future. We offer a choice of medical, dental and vision plans in all locations enabling team members to select the plans that best meet their family healthcare needs and budget. Micron also provides benefit programs that help protect your income if you are unable to work due to illness or injury, and paid family leave. Additionally, Micron benefits include a robust paid time-off program and paid holidays. For additional information regarding the Benefit programs available, please see the Benefits Guide posted on micron.com/careers/benefits.
Micron is proud to be an equal opportunity workplace and is an affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, citizenship status, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state, or local laws.
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To learn more about Micron, please visitmicron.com/careers
For US Sites Only: To request assistance with the application process and/or for reasonable accommodations, please contact Micron’s People Organization at [email protected] or 1-800-336-8918 (select option #3)
Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards.
Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron.
AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification.
Fraud alert: Micron advises job seekers to be cautious of unsolicited job offers and to verify the authenticity of any communication claiming to be from Micron by checking the official Micron careers website in the About Micron Technology, Inc.
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At Micron Technology, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $224,070 based on 236 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.
Micron Technology AI Hiring
Micron Technology has 7 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Architect. Positions span Boise, ID, US, Remote, US, Singapore, Singapore.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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