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About This Role
Are you looking for a unique opportunity to be a part of something great? Want to join a 17,000\-member team that works on the technology that powers the world around us? Looking for an atmosphere of trust, empowerment, respect, diversity, and communication? How about an opportunity to own a piece of a multi\-billion dollar (with a B!) global organization? We offer all that and more at Microchip Technology Inc.
People come to work at Microchip because we help design the technology that runs the world. They stay because our culture supports their growth and stability. They are challenged and driven by an incredible array of products and solutions with unlimited career potential. Microchip’s nationally\-recognized Leadership Passage Programs support career growth where we proudly enroll over a thousand people annually. We take pride in our commitment to employee development, values\-based decision making, and strong sense of community, driven by our Vision, Mission, and 11 Guiding Values; we affectionately refer to it as the *Aggregate System* and it’s won us countless awards for diversity and workplace excellence.
Our company is built by dedicated team players who love to challenge the status quo; we did not achieve record revenue and over 30 years of quarterly profitability without a great team dedicated to empowering innovation. People like you.
Visit our careers page to see what exciting opportunities and company perks await!
Job Description:
Job Description### We’re a small software team embedded within semiconductor manufacturing, focused on transforming how production data is collected, analyzed, and put into action across our sites. Our technology stack is evolving toward Python\-centric services, AI/ML\-powered analytics, and LLM\-driven tools, and we’re looking for an engineer to help drive that transformation.
### In this role, you’ll collaborate closely with test and yield engineers to turn complex manufacturing data into practical, user\-friendly systems. Your work will vary from developing data pipelines to prototyping LLM\-based solutions that streamline time\-consuming analysis. If you enjoy tackling diverse challenges, solving meaningful real\-world problems, and seeing your work deployed on the factory floor, this position offers a unique opportunity to make a direct impact.
- ### Build Python services, APIs, and data pipelines that collect, transform, and deliver semiconductor manufacturing data at scale.
- + Develop and deploy AI/ML models and LLM\-based solutions that automate analysis and surface actionable insights for engineers.
- + Design and maintain the data infrastructure (databases, messaging, web platforms) that the team and factory rely on daily.
- + Work directly with end\-users — test engineers, yield engineers, process engineers — to understand their problems and ship solutions.
- + Identify and implement automation opportunities with an AI/ML\-first mindset.
- + Maintain clear technical documentation so the team can grow without tribal knowledge.
Requirements/Qualifications:
Minimum Qualifications
- Bachelor's degree in Computer Science, Computer Information Systems, Mathematics, or a related field.
- Proficiency in Python — you've built real applications, pipelines, or tooling with it, not just scripts.
- Strong SQL skills including stored procedures and complex queries across relational databases.
- Working familiarity with AI/ML concepts and at least one of: ML frameworks (scikit\-learn, PyTorch, TensorFlow), LLM APIs, or AI\-assisted
development tools.
- Ability to translate business problems into technical solutions without needing everything spelled out.
\-
Preferred Qualifications
\*\*AI \& Data\*\*
- Hands\-on experience building or integrating LLM\-based solutions (RAG pipelines, prompt engineering, agent frameworks).
- Ability to effectively direct AI coding tools through clear intent and prompt articulation to produce production\-quality code.
- Experience with AI/ML model development, evaluation, and deployment in production environments.
- Knowledge of statistics, data mining, or data visualization techniques.
\*\*Infrastructure \& DevOps\*\*
- Docker/containerization and container orchestration.
- Event\-driven architectures and pub\-sub messaging (Redis, Kafka).
- Git, CI/CD pipelines (Azure DevOps, Jenkins), and test\-driven development practices.
- Linux/Unix command\-line proficiency; web server administration (NGINX) is a plus.
\*\*Domain\*\*
- Familiarity with semiconductor fabrication or testing processes.
- Experience with web front\-end technologies (JavaScript, React, Angular).
- Additional languages beyond Python (Java, C\#, C/C\+\+).
Travel Time:
No TravelPhysical Attributes:
Bending at Waist, Carrying, Climbing, Crawling, Crouching, Feeling, Foot Controls, Fumes or Odors, Handling, Hearing, Kneeling, Lifting, Other, Piece Work, Pulling, Pushing, Reaching, Seeing, Stooping, Talking, Works Alone, Works Around OthersPhysical Requirements:
15% walking, 20% standing, 65% sitting
Microchip Technology Inc is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.
For more information on applicable equal employment regulations, please refer to the Know Your Rights: Workplace Discrimination is Illegal Poster.
To all recruitment agencies: *Microchip Technology Inc.* *does not* *accept unsolicited agency resumes. Please do not forward resumes to our recruiting team or other Microchip employees. Microchip is not responsible for any fees related to unsolicited resumes.*
Role Details
About This Role
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.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Microchip Technology, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
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.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $213,800 based on 583 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
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.
Microchip Technology AI Hiring
Microchip Technology has 1 open AI role right now. They're hiring across AI Product Manager. Based in Chandler, AZ, US.
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 Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
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: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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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