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About This Role
A unique opportunity to join a highly visible business organization responsible for shaping onsemi's growth in next\-generation power conversion markets.
We are seeking a Product Line Manager to drive market strategy, product direction, customer engagement, and business growth for power semiconductor solutions focused on AI Datacenter Infrastructure, Power Supplies, Energy Storage, Solid State Transformers, and advanced Power Conversion systems.
This role sits at the intersection of technology, business strategy, product management, sales, and marketing, translating market requirements and system\-level trends into product roadmaps, investment priorities, and growth strategies. The successful candidate will leverage deep knowledge of power electronics and semiconductor technologies to identify growth opportunities, drive new product initiatives from business planning through product release, and help position onsemi as a leader in Silicon Carbide (SiC) and advanced power solutions for AI Datacenter Infrastructure.
- Responsibilities
- Serve as a subject matter expert for AI Datacenter Infrastructure, Datacenter Power, Energy Storage, Solid State Transformers, and advanced Power Conversion systems.
- Monitor and analyze market trends, competitive dynamics, emerging architectures, and customer requirements.
- Identify strategic growth opportunities across:
- AI Datacenters
- Power Supply Units (PSU)
- Intermediate Bus Converters (IBC)
- Battery Backup Units (BBU)
- Power Distribution Units (PDU)
- Energy Storage Systems (ESS)
- Solid State Transformers (SST)
- Grid Infrastructure
- Develop system\-level understanding of customer challenges, technology transitions, and market dynamics.
- Establish onsemi as a thought leader in next\-generation AI power delivery architectures.
Strategic Planning \& Product Direction
- Define and execute product strategies, technology roadmaps, and growth plans aligned with business objectives.
- Translate market requirements into actionable product and technology roadmaps.
- Influence product definition through close collaboration with Engineering, Applications, Sales, and Business Units.
- Own business planning, TAM/SAM/SOM analysis, revenue forecasting, product positioning, and long\-term growth strategies.
- Present, propose, and actively drive new product and business opportunities from concept through product release.
- Develop business cases, investment proposals, and go\-to\-market strategies for new opportunities.
- Work closely alongside Engineering, Operations, Sales, Marketing, and NPI teams to ensure successful execution and commercialization of new products.
Product Line Ownership
- Own product strategy, roadmap, business growth, and profitability objectives.
- Drive portfolio expansion and investment prioritization based on market opportunities and customer requirements.
- Define pricing, market positioning, and go\-to\-market strategies.
- Monitor product performance, market share, competitive positioning, and profitability.
- Lead cross\-functional business reviews and decision making to maximize growth and return on investment.
- Develop revenue forecasts and long\-range business plans.
Customer \& Ecosystem Engagement
- Engage directly with strategic customers, hyperscalers, OEMs, ODMs, and system providers.
- Support executive and technical customer engagements.
- Gather Voice\-of\-Customer feedback and translate it into product and business requirements.
- Build strategic relationships across the AI Datacenter and Power Electronics ecosystem to identify emerging opportunities and partnerships.
Technical Positioning \& Content Development
- Translate technical capabilities into compelling customer value propositions.
- Develop competitive positioning, market intelligence, and differentiation strategies.
- Create business and technical collateral including:
- Market Assessments
- Business Cases
- Product Roadmaps
- System Architecture Guides
- Executive Presentations
- Support industry events, conferences, customer workshops, and strategic engagements.
Leadership
- Lead and influence cross\-functional teams without direct authority to achieve business objectives.
- Build alignment across Product Management, Engineering, Applications, Sales, Operations, and Executive Leadership.
- Drive accountability, execution discipline, and customer focus across complex initiatives.
- Champion strategic initiatives and drive organizational alignment around key business priorities.
- Mentor junior team members and provide leadership in strategic planning activities.
- Effectively communicate vision, priorities, and business strategy across all organizational levels, including executive leadership.
- Qualifications
MSEE, or equivalent technical degree.
- 12\+ years of experience in Product Line Management, Product Marketing, Technical Marketing, Applications Engineering, Business Development, or related roles.
- Strong understanding of power electronics and power conversion architectures.
- Experience with one or more semiconductor technologies:
- Silicon Carbide (SiC)
- Gallium Nitride (GaN)
- Power MOSFETs / IGBTs
- Power Discretes
- Power Modules
- Strong knowledge of AI Datacenter power architectures including PSU, IBC, BBU, PDU, rack power delivery, and next\-generation power distribution architectures.
- Knowledge of Energy Storage Systems (ESS), Solid State Transformers (SST), and Grid Infrastructure is highly desirable.
- Demonstrated ownership of product strategy, business planning, pricing, forecasting, and product roadmap development.
- Strong business acumen with the ability to connect technology trends to market opportunities.
- Proven experience driving cross\-functional projects from business planning through product introduction and commercialization.
- Strong leadership, influencing, communication, and executive presentation skills.
- Ability to analyze market, technical, and financial data and develop strategic recommendations.
Preferred
- MSEE or MBA.
- Experience in AI Datacenter Power Infrastructure.
- Experience in Energy Storage Systems (ESS), Solid State Transformers (SST), and Grid Infrastructure.
- Familiarity with semiconductor device physics and power semiconductor manufacturing technologies.
- Experience authoring market studies, business plans, technical articles, or conference presentations.
- Demonstrated success managing product portfolios and driving revenue growth.
Travel
- Up to 20% travel, including customer visits, industry conferences, and internal strategy meetings globally.
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. Mid-level AI roles across all categories have a median of $194,400.
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
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