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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.
At Micron, our Technology Development team sits at the center of innovation. We build advanced modeling and AI solutions that shape how memory and storage technologies are designed, built, and scaled. Working at the intersection of physics, data, and engineering, we turn complex problems into practical breakthroughs that matter!
This role is critical to how we accelerate semiconductor technology development using advanced AI and physics\-based modeling. You will lead the design and deployment of agentic and LLM\-powered solutions that directly influence process technology, packaging, and early manufacturing decisions. If you enjoy driving hard problems end to end and collaborating across hardware and software teams, this role offers real impact and visibility. We are hiring for more than one role spanning Senior/Staff/Principal/Technical Staff Engineer.
Responsibilities:
- Design and deploy physics\-based, ML, and LLM\-powered modeling solutions for semiconductor process, package, and manufacturing workflows
- Architect agentic AI systems that integrate with modeling tools, design automation environments, and fab/test platforms to enable digital twin development
- Define and promote best known methods for deploying LLMs and agentic systems reliably in production environments
- Evaluate and improve model performance using methods such as structured benchmarking, prompt tuning, RLHF, and feedback loops
- Translate modeling results into clear insights through data storytelling and visualizations for technical and non\-technical audiences
Minimum Qualifications:
- Bachelors, Masters or PhD in Electrical Engineering, Computer Science, or a related field with experience commensurate with the role.
- Experience in developing machine learning models on large and diverse datasets.
- Hands\-on experience building and deploying AI/ML systems using frameworks such as PyTorch or TensorFlow, including LLMs, RAG, and agentic workflows
- Experience deploying ML pipelines on cloud platforms such as AWS, GCP, or Azure
Preferred Qualifications:
- Proven ability to lead AI/ML projects from problem definition through production in semiconductor environments. Applying AI/ML to semiconductor design, process technology, validation, or manufacturing workflows
- Experience with MLOps practices, CI/CD pipelines, and production LLM deployment
- Strong understanding of agentic AI frameworks such as LangGraph or AutoGen and evaluation tools such as AgentEval
- Experience with Agentic Architecture development, MCPs, and building Knowledge Base / Ontologies from disparate data sources
- Demonstrated ability to partner with cross\-functional engineering teams and turn complex models into actionable decisions
- Strong Communication skills
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 visit micron.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 hrsupport\[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
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Micron Technology, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Micron Technology AI Hiring
Micron Technology has 6 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Boise, ID, US, San Jose, CA, US. Compensation range: $290K - $331K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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