Interested in this AI/ML Engineer role at Micron Technology?
Apply Now →Skills & Technologies
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.
About our Team:
We are seeking an experienced AI Full Stack Engineer to build modern applications where AI is a first-class capability—both in the development workflow and in production systems. This role focuses on designing and integrating AI-powered solutions using custom agents, Model Context Protocol (MCP), and scalable software engineering practices to accelerate development and deliver reliable, production-grade systems. Our team needs additional support to scale with the increased SMAI Data Science footprint and expand software support for Technology Development workflows and AI enablement.
Position Overview:
We are looking for an AI Full Stack Engineer who builds modern applications using AI as a first-class capability—both as part of the development workflow and the systems we ship. This role goes beyond simply using AI coding assistants. It requires strong understanding of advanced timely creation, vibe scripting, Rework Rate Reduction, and applying Custom Agents with integrations built around MCP or equivalent experience. The goal is to significantly shorten development and feedback cycles by automating routine engineering work, augmenting human decision-making, and embedding intelligence into development workflows and delivered applications.
Responsibilities:
- Contribute to platform enablement, architecture decisions, code reviews, and shared engineering standards; design, build, test, deploy, and operate full stack software solutions while owning features end‑to‑end including system design, implementation, testing, observability, and lifecycle support, and collaborating with business teams to translate requirements into scalable solutions
- Build and implement Custom AI Agents to support workflows like task automation, decision support, summarization, multi-step reasoning, and guided execution. Show proficiency in timely engineering and Vibe Coding prototypes.
- Build and integrate systems using the Model Context Protocol (MCP) to enable structured context sharing between models, tools, and agents, tool and capability exposure to LLM‑based agents, and interoperability across AI components without hard‑coded integrations
- Integrate applications with external and internal AI services (LLMs, embeddings, search, tools) via well‑designed APIs, and harden AI components for 24x7 critical physical manufacturing systems with attention to reliability, resiliency, latency, performance, security, permissions, data boundaries, observability, and maintainability while applying retrieval‑augmented generation (RAG) systems
- Use AI‑assisted tools to accelerate development (code generation, refactoring, test creation, documentation), critically evaluate and refine AI‑generated output, and apply human judgment to architecture, correctness, and long‑term maintainability
- Conduct requirements gathering and analysis to understand software domains, interfaces, and characteristics; write code using programming, scripting, and database languages; support testing, deployment, maintenance, and evolution; and follow guidelines including coding standards, code reviews, source control, build processes, testing, and operations.
Minimum Qualifications:
- Strong foundation in full stack software engineering fundamentals, including object‑oriented design, clean code practices, testing strategies, and long‑term maintainability
- Proven experience building and operating production‑grade backend or full‑stack systems, including API design, service integration, and data persistence
- Ability to design and evolve scalable system architectures with tradeoffs across performance, reliability, security, and developer productivity
- Experience working in cloud‑native environments, including CI/CD pipelines, containerized deployments, and modern operational practices
- Solid understanding of data modeling and storage technologies (relational and non‑relational) and selecting the right tool for the problem
- Track record of owning software end‑to‑end from design through lifecycle support and collaborating across engineering and business teams
- Experience with technologies such as C#, ASP.NET, Angular, RESTful APIs, SQL and NoSQL databases, and Kubernetes‑based platforms
- Experience designing and implementing Custom AI agents beyond simple chat interfaces, including developing skills for custom agents
- AI‑native skills such as Prompt Engineering, Vibe Coding, context window management, and chain‑of‑thought design
- Experience integrating systems using MCP or equivalent structured agent/tool interfaces and strong understanding of prompt design and tool invocation
- Ability to reason about hallucination risk, failure modes, permissions, and guardrails in AI‑enabled systems, and hands‑on experience integrating RAG‑based systems
Preferred Qualifications:
- Experience applying AI‑enabled development practices to accelerate engineering workflows and improve productivity
- Demonstrated ability to critically evaluate AI‑generated outputs and refine them for production‑grade systems
- Experience building interoperable AI systems without hard‑coded integrations
- Strong understanding of operationalizing AI in critical environments with high reliability and performance requirements
Kubernetes / Platform Engineering:
- A strong interest in Kubernetes and cloud-native infrastructure. You enjoy running, upgrading and troubleshooting production clusters and want to deepen your expertise in container orchestration, GPU workloads and platform reliability.
- Experience designing and running distributed systems on Kubernetes. You understand message-driven architectures, horizontal scaling, fault tolerance and multi-tenant workloads, and you'll help shape the roadmap for a platform that processes large volumes of data across sites.
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.
To learn about your right to work click here.
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
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 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.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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).
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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.