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About This Role
Job Details:
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Job Description:
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Join Intel and build a better tomorrow .
Manufacturing Development Customer Engineering (MDCE) is Intel's newest organization within Intel Foundry Technology Manufacturing (FTM). We bridge the critical gap between Technology Development (TD) and High Volume Manufacturing (HVM), accelerating technology readiness, manufacturing maturity, yield improvement, and operational excellence across Intel's product and foundry ecosystem. MDCE partners across engineering organizations to transform manufacturing insights into scalable production solutions that enable Intel's foundry strategy and future growth.
As a Senior AI / Data Science Engineer in the MDCE Data Science organization, you will play a critical role in shaping the future of AI\-driven semiconductor manufacturing. You will lead the development, deployment, and adoption of advanced Artificial Intelligence (AI), Machine Learning (ML), and Generative AI technologies that accelerate yield learning, improve process optimization, and increase engineering productivity across Intel Foundry.
The successful candidate will combine deep expertise in semiconductor manufacturing analytics, AI/ML methodologies, and software engineering with the ability to influence cross\-functional stakeholders and drive innovation at scale.
Key Responsibilities:
- Lead development of advanced AI, Machine Learning, and Generative AI solutions that address critical semiconductor manufacturing and yield challenges.
- Drive adoption of AI technologies across MDCE and partner engineering organizations, enabling data\-driven decision making and measurable business impact.
- Develop and deploy Large Language Model (LLM) and Generative AI applications that improve engineering productivity, accelerate troubleshooting, and enhance knowledge discovery.
- Analyze large\-scale manufacturing datasets to identify yield detractors, process excursions, root causes, and optimization opportunities.
- Design and implement scalable data pipelines, analytics frameworks, and AI infrastructure that enable robust and sustainable manufacturing analytics.
- Collaborate closely with Yield Engineering, Process Integration, Manufacturing, and Technology Development teams to solve complex semiconductor challenges.
- Establish best practices for AI model development, software quality, deployment, monitoring, and lifecycle management.
- Lead cross\-functional technical initiatives and influence strategic AI roadmaps across multiple engineering organizations.
- Mentor engineers and contribute to the growth of Intel's technical AI and Data Science community.
- Support Intel Foundry's manufacturing excellence objectives through innovative applications of AI, machine learning, and advanced analytics.
Core Competencies
- Ability to collaborate effectively within a team setting.
- Excellent Communication Skills.
Qualifications:
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The Minimum qualifications are required to be initially considered for this position. Minimum qualifications listed below would be obtained through a combination of industry relevant job experience, internship experience and / or schoolwork/classes/research. The preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.
Minimum Qualifications
- Bachelor's degree in Computer Science, Data Science, Electrical Engineering, Industrial Engineering, Statistics, Applied Mathematics, or in a STEM\-related field.
- 6\+ years of industry experience in one or more of the following: Data Science, Machine Learning, Artificial Intelligence, Advanced Analytics. Performing yield analysis, root\-cause analysis, process engineering analytics, and Statistical Process Control (SPC).
- 5\+ Years of Semiconductor industry experience with manufacturing, process, and yield\-related challenges.
- Strong programming experience in Python and SQL including developing scalable data pipelines and production\-quality analytical solutions.
Experience leading cross\-functional technical projects and driving business impact through data\-driven solutions.
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Preferred Qualifications
- Post graduate degree in Computer Science, Data Science, Electrical Engineering, Industrial Engineering, Statistics, Applied Mathematics, or in a STEM\-related field.
- Experience driving AI transformation initiatives across large engineering organizations.
- Experience developing Generative AI applications using Retrieval Augmented Generation (RAG) and Agentic AI architectures.
- Experience deploying AI/ML solutions into production environments.
- Experience developing APIs and microservices.
- Semiconductor foundry experience.
- Background in Yield Engineering, Process Integration, Process Development, or Manufacturing Engineering.
Proven track record mentoring engineers and providing technical leadership
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Job Type:
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Experienced Hire
Shift:
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Shift 1 (United States of America)
Primary Location:
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US, Arizona, Phoenix
Additional Locations:
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US, Oregon, Hillsboro
Posting Statement:
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All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
Position of Trust
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N/A
Benefits
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We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation. Find out more about the benefits of working at Intel .
Annual Salary Range for jobs which could be performed in the US: $136,900\.00\-269,150\.00 USD
The range displayed on this job posting reflects the minimum and maximum target compensation for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training. Your recruiter can share more about the specific compensation range for your preferred location during the hiring process.
Work Model for this Role
This role will be eligible for our hybrid work model which allows employees to split their time between working on\-site at their assigned Intel site and off\-site. \* Job posting details (such as work model, location or time type) are subject to change.
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ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.
Salary Context
This $136K-$269K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Intel Corporation, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($203K) sits 6% below the category median. Disclosed range: $136K to $269K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Intel Corporation AI Hiring
Intel Corporation has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer. Positions span Santa Clara, CA, US, Folsom, CA, US, Phoenix, AZ, US. Compensation range: $153K - $315K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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