Senior Director, AI NPD Platforms & Engineering Intelligence
onsemi · San Jose, CA
- Level
- Director+
- Team
- Manufacturing
- Base pay
- $217k–$390k
- Degree
- Required
- Experience asked
- 15+ years
- Industry
- Industrial
- Posted
- 30+ days ago
The role in 30 seconds
A director-level manufacturing role at onsemi (industrial) in San Jose, CA. With a highly differentiated and innovative product portfolio, onsemi creates intell.
- They ask for
- 15+ years · degree required
- Tools it names
- Validation (IQ/OQ/PQ), Automation
- Base pay
- $217k–$390k
Read from the posting. The full description is below.
From the plant jobs report · week of November 2, 2026
Manufacturing Engineer roles in California: typical posted base $112k–$168k. This one lists $217k–$390k.
Median of the low and high ends of 147 live postings that list pay, out of 388 manufacturing engineer openings in California, read from manufacturers' own careers sites.
California plant jobs report →Pay by team and state →What moves pay →
How this role compares
Against 387 other live manufacturing postings in California.
- Base pay
- About 128% above similar roles
- Experience asked
- More than usual (15+ vs 5+)
Typical $110k–$157k, from 127 postings that list pay.
Similar roles usually ask 5+ years.
Skills these teams ask for most
- Tooling 52%
- New product launch 47%
- Root cause 38%
- ✓ Automation 35%
- CATIA / NX 27%
- Machining 27%
✓ = this posting asks for it too. % = share of similar postings that mention it.
At a glance
Pulled from the posting text. If it's not listed here, the posting didn't say.
How to get in at this plant
Five moves over a week or two. The first one takes ten minutes.
- 1
Pick 2–3 people on the Manufacturing team at onsemi
Engineers who joined in the last year or two reply the most. Use the team search on this page to find them.
- 2
Send a short, curious note
One real question about their work. No favors yet. Each person gets a different note, written for this role.
- 3
Turn a reply into a short call
Questions worth asking about this team:
- What does a normal week look like on your team?
- Is the work mostly new product launches or sustaining existing lines?
- How much time do you spend on the floor versus at a desk?
- How did you land the job? What stood out in your interviews?
- 4
Apply with someone vouching for you
If the call went well, ask whether they’d pass your résumé to the hiring manager. Send your résumé link, apply that day, and let them know.
- 5
Bring proof to the interview
One page of before-and-after numbers from projects you led: scrap, OEE, cycle time, cost. Plant managers hire on results.
This plant: onsemi in San Jose
1 other open role at this site.
- Principal Reliability Engineer · Reliability · Senior
Full description from onsemiAs posted on their careers site.ShowHide
requirements
agents
- ✓Program management agents
- ✓Define standards for agent architecture, interoperability, orchestration, security, and lifecycle management.
- ✓Partner with engineering, CAD, IT, and infrastructure teams to deploy scalable AI design environments.
requirements
- ✓Auditability and traceability
- ✓Ensure responsible deployment of AI systems across engineering organizations.
Required Qualifications
- ✓Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, or related technical discipline.
- ✓15+ years of semiconductor industry experience.
- ✓10+ years of leadership experience in product development, EDA, design methodology, CAD, semiconductor software, or AI-enabled engineering environments.
- ✓Deep understanding of semiconductor development workflows including:
- ✓Analog design
- ✓Digital design
- ✓Verification
- ✓Physical design
- ✓Design automation
- ✓Semiconductor manufacturing flows
- ✓Experience leading complex cross-functional engineering organizations.
- ✓Proven ability to influence executive leadership and drive organizational transformation.
Preferred Qualifications
- ✓Advanced degree (MS or PhD) in Electrical Engineering, AI, Machine Learning, Computer Science, or related field.
- ✓Experience developing AI-enabled EDA tools, semiconductor design automation platforms, or Agentic AI systems.
- ✓Hands-on experience with:
- ✓LLMs
- ✓Retrieval-Augmented Generation (RAG)
- ✓Multi-Agent Architectures
- ✓Model Context Protocol (MCP)
- ✓Workflow Orchestration Platforms
- ✓Reinforcement Learning
- ✓AI Copilots
- ✓Knowledge Graphs
- ✓Background in organizational AI transformation programs.
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 intell
Show full description ↓Show less ↑
onsemi is seeking an industry leading AI and semiconductor technology executive to define, build, and govern the next generation of AI-enabled New Product Development (NPD).
This leader will establish the vision, strategy, operating model, and governance framework for Agentic AI across the semiconductor design lifecycle, enabling AI-assisted and AI-driven development of analog, mixed-signal, power management, sensing, memory, verification, layout, test, packaging, and system architectures.
The role will serve as the executive authority for Agentic AI Design Engineering and will lead the transformation of semiconductor development workflows through reusable AI agents, advanced reasoning systems, design knowledge repositories, and AI-enabled engineering methodologies.
Success in this role will materially improve engineering productivity, accelerate product development cycles, increase IP reuse, enhance design quality, and establish onsemi as an industry leader in AI-enabled semiconductor development.
Define Enterprise AI NPD Vision
- •Develop and execute the long term strategy for Agentic AI-enabled semiconductor product development.
- •Create the roadmap for AI deployment across product planning, architecture, design, verification, physical implementation, characterization, test, reliability, applications, and documentation.
- •Define the maturity model for AI-assisted, AI-enabled, and AI-native development methodologies.
- •Establish objectives, success metrics, and business outcomes tied to engineering productivity and NPD execution.
Build the Agentic Design Platform
- •Lead creation of an enterprise Agentic AI framework supporting:
- •Analog design agents
- •Digital design agents
- •Verification agents
- •Layout agents
- •Test engineering agents
- •Applications engineering agents
- •Documentation and
Govern AI Agents as Strategic IP
- •Establish companywide governance for AI agents and engineering knowledge assets.
- •Develop processes for agent qualification, validation, release, version control, maintenance, retirement, and reuse.
- •Create an "Agent Library" and corporate repository of approved agents, prompts, workflows, and design knowledge.
- •Define review and approval processes for new internally developed agents.
- •Ensure cross division reuse of agents.
Transform Analog and Digital Design
- •Define AI-enabled methodologies across:
- •Circuit design
- •Architecture exploration
- •RTL development
- •Verification
- •Physical design
- •Device characterization
- •Design reviews
- •Failure analysis
- •Documentation generation
- •Drive deployment of AI workflows into production engineering environments.
- •Identify high-value use cases capable of delivering significant improvements in development cycle time, engineering efficiency, and design quality.
Create the AI Governance Operating Model
- •Define AI governance policies for engineering workflows.
- •Establish standards for:
- •Model selection
- •Training data usage
- •Intellectual property protection
- •Security and access controls
- •Human-in-the-loop decision processes
- •Validation
Drive Measurable Business Outcomes
- •Establish KPI frameworks measuring:
- •NPD cycle time
- •Design productivity
- •Engineering efficiency
- •Agent reuse rates
- •AI adoption
- •Design quality improvements
- •Verification coverage improvements
- •IP creation velocity
- •Deliver measurable improvements in development throughput and engineering effectiveness.