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About This Role
Job Details:
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Job Description:
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The Role and Impact
Are you ready to take the first step toward a rewarding career in technology? Intel is offering an exciting opportunity to join us as an Industry Immersion Intern. In this role, you will gain hands\-on experience, develop practical skills, and contribute to meaningful projects that support Intel's business objectives. You will work in a collaborative environment alongside industry experts, gaining exposure to Intel's innovative product development processes and exploring future career opportunities. This unique internship program is designed to immerse you in the fast\-paced world of technology, offering a glimpse into the impact you can make as part of Intel's mission to create world\-changing technology.
Key Responsibilities
- Actively participate in hands\-on projects that align with Intel's business goals, spanning product development and innovation.
- Learn and apply technical knowledge and skills to solve real\-world challenges.
- Support IP (Intelectual Property) blocks design activities, including developing and maintaining of flows, tools and methodologies.
- Collaborate with cross\-functional teams to support business initiatives and drive results.
- Engage in professional development opportunities to build a strong foundation for your future career.
This position is not eligible for Intel immigration sponsorship.
Qualifications:
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Minimum qualifications are required to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates. Requirements listed would be obtained through a combination of industry relevant job experience, internship experiences and or schoolwork/classes/research.
Minimum Qualifications
- Enrolled or recently acquired Bachelor's degree program in Electrical Engineering, Electronic Engineer, Computer Engineering, or a related field.
At least 6 months of experience on the following, education may be considered:
- Comfortable writing Python \- data types, control flow, functions, and object\-oriented programming.
- Working knowledge of Git and CI/CD workflows like Github Actions.
- Basic Knowledge of relational databases (PostgreSQL or SQLite) and basic SQL.
Experience using at least one AI coding tool such as Claude, GitHub Copilot, or Cursor, and the ability to critically evaluate its output.
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Preferred Qualifications:
- Experience with Django REST Framework \- building serializers, view sets, and token\-authenticated APIs.
- Ability to build and style web pages using HTML, CSS.
- Familiarity with Tailwind CSS and Alpine.js for building interactive server\-rendered UIs without heavy frontend frameworks.
- Exposure to OAuth2 or SSO authentication flows.
- Familiarity with REST API consumption \- making authenticated requests, paginating results, and handling JSON responses.
- Exposure to PostgreSQL, including basic query tuning or understanding of indexes, joins, and migrations.
- Familiarity with task tracking tools like Jira, GitHub Issues, or similar.
- AI Agents, Skills, and Tools.
- Strong candidates will know how to write effective prompts that produce production\-ready code from AI models.
- They will be comfortable using agentic coding tools like GitHub Copilot Agent Mode, Claude Code, or Cursor to scaffold features end\-to\-end, wire up REST APIs and external data services into a Django project, and \- most importantly \- review, test, and take full ownership of AI\-generated code before it ships.
Familiarity with writing custom Copilot instructions, skills, or agent configurations is a plus.
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Job Type:
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Student / Intern
Shift:
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Shift 1 (United States of America)
Primary Location:
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US, California, Folsom
Additional Locations:
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Business group:
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Silicon and Platform Engineering Group (SPE): Deliver breakthrough silicon and platform solutions that deliver industry\-leading products today while also defining the next generation of computing experiences.
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: $69,800\.00\-94,500\.00 USD (Hourly Role)
The range displayed on this job posting reflects the minimum and maximum target compensation for the position across all US locations. Our standard internship rates are based on your degree, location, and the job role. Your recruiter can share more about the specific compensation range for your preferred location and job role during the hiring process.
Work Model for this Role
This role will require an on\-site presence. \* 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 $69K-$94K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($82K) sits 54% below the category median. Disclosed range: $69K to $94K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Intel Corporation AI Hiring
Intel Corporation has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Folsom, CA, US. Compensation range: $94K - $96K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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