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About This Role
An internship at onsemi offers a dynamic opportunity to gain hands-on experience through real-world projects that drive innovation in the semiconductor industry. Interns are entrusted with meaningful responsibilities and exposed to cutting-edge technologies, fostering both technical skill development and professional growth.
onsemi views internships as a strategic pipeline for entry-level career positions, providing a collaborative and inclusive environment where interns work alongside diverse teams and contribute to high-impact initiatives. The company’s commitment to sustainability, including its goal of achieving net-zero emissions by 2040, enables interns to engage in projects that align with global environmental priorities.
Key benefits include:
- Competitive compensation and medical benefits
- Flexible work hours aligned with academic schedules
- Access to Employee Resource Groups (ERGs) which foster a sense of belonging and provide opportunities for mentorship and community engagement
- Networking events, professional development workshops, and project showcases that build career readiness and visibility across the organization
Internship roles span multiple disciplines including engineering, business operations, data analytics, and finance, and are designed to align with onsemi’s strategic goals of innovation and workforce development. Structured programs such as rotational field sales and finance analyst tracks offer additional career pathways for interns transitioning into full-time roles.
Responsibilities
onsemi is seeking a highly motivated AI & Data Engineering Intern (Onsite – San Jose) to help accelerate new product introduction by building and improving AI‑assisted internal workflows that speed up go‑to‑market (GTM) and new product introduction (NPI) execution. The intern will contribute to AI tools that streamline and make repeatable the creation of launch deliverables spanning web readiness, technical & marketing documentation, training content, samples/channel readiness, and digital promotional materials.
A key focus area is supporting AI‑driven collateral generation - an internal capability intended to generate first drafts of items such as press releases and NPI presentations, using approved “sources of truth”.
This role is ideal for a student with strong AI aptitude and the willingness to learn and build practical solutions using AI‑assisted programming, while producing clear documentation and measurable outcomes that reduce cycle time and manual effort in GTM/NPI workflows.
Qualifications
Qualifications – External Requirements:
Currently enrolled in a Bachelor’s (Junior/Senior) or Master’s program in Electrical/Electronics Engineering, Computer Engineering, or Data Science/AI/ML (returning to school after internship).
Demonstrated aptitude to use AI effectively (rapid learning, iteration discipline, structured prompting/testing mindset) and willingness to program using AI tools to produce working scripts/workflows and documented outcomes.
Strong communication skills with the ability to produce clear, structured documentation and translate ambiguous workflows into repeatable, auditable steps (critical for GTM/NPI readiness and internal tool adoption).
Preferred (Nice‑to‑Have):
Familiarity with Python and basic data tooling (e.g., data wrangling/analysis, automation scripting) to support internal workflow automation and repeatable reporting.
Exposure to LLM/prompt workflows and/or document‑to‑output generation concepts (e.g., generating PowerPoint/Word outputs from structured prompts and validated source documents).
Interest in product planning and launch processes (PPP/WPP GTM/NPI) and comfort working across deliverable categories (documentation, training, web readiness, and launch checklists).
Here at onsemi we take great pride in our internship program and the efforts we take to provide students with hands-on industry experience. We provide competitive pay medical benefits, various networking event opportunities, and flexible hours based on school schedule. onsemi is excited to share the base salary range for this position is $25.60/hr. The final pay rate for the successful candidate will depend on geographic location, skills, education, experience, and/or consideration of internal equity of our current team members. We also offer a competitive benefits package. https://www.onsemi.com/site/pdf/Benefits-Summary-USA.pdf
About Us
onsemi (Nasdaq: ON) is driving disruptive innovations to help build a better future. With a focus on automotive and industrial end-markets, the company is accelerating change in megatrends such as vehicle electrification and safety, sustainable energy grids, industrial automation, and 5G and cloud infrastructure. With a highly differentiated and innovative product portfolio, onsemi creates intelligent power and sensing technologies that solve the world’s most complex challenges and leads the way in creating a safer, cleaner, and smarter world.
More details about our company benefits can be found here: https://www.onsemi.com/careers/career-benefits
About the Team
We are committed to sourcing, attracting, and hiring high-performance innovators, while providing all candidates a positive recruitment experience that builds our brand as a great place to work.
onsemi is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, ancestry, national origin, age, marital status, pregnancy, sex, sexual orientation, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, or any other protected category under applicable federal, state, or local laws.
If you are an individual with a disability and require a reasonable accommodation to complete any part of the application process, or are limited in the ability or unable to access or use this online application process and need an alternative method for applying, you may contact [email protected] for assistance.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At onsemi, 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 $212,000 based on 11,866 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
onsemi AI Hiring
onsemi has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Jose, CA, San Jose, CA, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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