Interested in this AI/ML Engineer role at onsemi?
Apply Now →Skills & Technologies
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.
The Tax Technology Intern role supports the tax department by helping improve how tax data, tools, and workflows are organized, automated, and maintained. Prior tax experience is not required. The ideal candidate is curious, technology\-oriented, and willing to learn how tax processes work while applying automation, analytics, AI, and process\-improvement tools to strengthen the department’s technology environment. Success in this role requires a strong learning mindset, attention to detail, problem\-solving ability, and interest in using technology to make work more efficient, accurate, and repeatable.
- Responsibilities
- Assist with automation and process\-improvement projects that support tax compliance, provision, reporting, and department operations.
- Help organize, clean, validate, and reconcile tax\-related data from Excel workbooks, SharePoint lists, databases, and other systems. ·
- Support the development and maintenance of repeatable workflows using tools such as Excel, Power Query, Power BI, SharePoint, Power Automate, Copilot, or similar automation platforms.
- Document current\-state tax processes and help identify opportunities to reduce manual work, improve controls, and create more consistent outputs.
- Assist with building dashboards, trackers, templates, and data models that improve visibility into tax workstreams, deadlines, status, and key metrics.
- Test new or enhanced automation workflows, summarize issues, and help refine tools based on feedback from tax team members.
- Support the tax team’s use of AI and emerging technologies by helping draft prompts, organize reference materials, and identify practical use cases.
- Prepare clear documentation, user guides, and simple training materials for new tools, workflows, and technology\-enabled processes.
- Contribute to ad\-hoc tax technology, data, and process\-improvement projects as needed.
Qualifications
Qualifications – External
Requirements:
- Interest in automation, data analytics, AI tools, and technology\-enabled process improvement.
- Working knowledge of Microsoft Excel; exposure to tools such as Power Query, Power BI, SharePoint, Power Automate, databases, scripting, or similar platforms is helpful.
- Ability and willingness to learn unfamiliar systems, tools, and business processes quickly.
- Strong analytical, organizational, and problem\-solving skills.
- Strong attention to detail and commitment to producing accurate, reliable work.
- Clear written and verbal communication skills, including the ability to document processes and explain technology\-enabled solutions in plain language.
- Collaborative mindset with the ability to work independently, ask thoughtful questions, and manage priorities effectively.
Qualifications:
- Currently pursuing a degree in Accounting, Finance, Business, Computer Science, Artificial Intelligence, Data Science, Engineering, Information Systems, or a related field.
- Interest and curiosity in learning how technology can improve tax processes.
- Some familiarity with general automation, analytics, reporting, database, or workflow tools is helpful.
- Demonstrated curiosity and willingness to apply new technologies to strengthen the tax department’s technology setup.
- Reliable, detail\-oriented, and comfortable working with data, deadlines, and evolving project requirements.
- Ability to work on site at onsemi’s headquarters in Scottsdale, Arizona .We are open to working around school schedules to meet the Company and right candidate’s needs.
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.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000.
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.
onsemi AI Hiring
onsemi has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Scottsdale, AZ, US, San Jose, CA, US.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.