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Job Description
We are seeking an experienced Director of Mass Market to lead our strategic expansion into mass market segments. Mass market expansion is a key corporate initiative at Renesas. Reporting to the High-Performance AI and Compute (HPAC) Product Line Head, you will own the complete transformation of the product line operations, infrastructure, and go-to-market strategy to serve mass market customers.
The HPAC product line—part of the Performance Computing Power (PCP) division—develops high-performance digital multiphase voltage regulator solutions for AI, cloud infrastructure, data centers, servers, networking, and emerging computing markets. As Director, you will define the mass market product strategy and roadmap, build and lead a high-performing team of product managers and marketing specialists at various experience levels, and execute the operational transformation to unlock mass market opportunity at scale.
Mass market represents a significant growth opportunity for HPAC, not only to expand our revenue streams to mass market, but also to improve our operational efficiencies supporting our entire customer base. This role is fundamental to unlocking that potential.
This newly created director role is a pivotal opportunity to shape the future of power solutions in one of the fastest growing product lines in Renesas. This is a critical leadership role that will shape the future growth trajectory of our product line.
Strategic Planning & Operations Transformation
- Develop a comprehensive operational plan to enable mass market reach, including required infrastructure, organizational structure, processes, and procedures.
- Identify gaps in current operations and design scalable solutions that support mass market scale.
- Establish key performance metrics and success criteria for mass market penetration
- Present quarterly roadmap and progress updates to executive leadership
Product & Operations Scaling
- Partner with Product Management, Product Development, and Applications Engineering teams to align product roadmap with mass market requirements
- Lead cross-functional efforts to adjust design, manufacturing, and support operations for cost-competitiveness and scalability
- Ensure product quality, reliability, and support meet mass market customer expectations
- Manage product portfolio rationalization to optimize for mass market segments
Distribution Channel Strategy & Enablement
- Build and manage relationships with distribution partners
- Lead the development of channel enablement programs, training, and marketing support to drive Partner success
- Define channel margins, pricing, and incentive structures that drive volume growth
- Monitor channel performance and adjust strategies to meet volume and margin targets
Market Expansion & Customer Programs
- Define target mass market segments and develop go-to-market strategies for each.
- Lead customer programs to support design-ins, volume ramps, and long-term partnerships
- Identify and overcome barriers to mass market adoption (price, support, delivery, etc.)
Website & Digital Enablement
- Drive evolution of company website to support mass market self-service (documentation, design tools, support resources)
- Enable e-commerce and direct sales capabilities for mass market customers
- Ensure digital tools reduce friction in customer acquisition and support
Team Leadership & Development
- Build and Hire: Recruit and develop a team of product managers and marketing specialist with diverse experience levels to support the mass market. Establish hiring criteria and interview processes aligned with organizational needs.
- Drive Accountability: Define roles, responsibilities, and success metrics for each team member. Establish clear ownership of products and outcomes. Hold team members accountable to commitments.
- Build Culture: Foster a collaborative, innovative, and high-performing team environment. Encourage knowledge-sharing across the organization. Instill sense of urgency and ownership. Lead by example in customer-first mindset.
Qualifications
Experience
- 15+ years in power semiconductors or related semiconductor markets with direct experience supporting mass market customers
- Proven success scaling operations from enterprise/OEM focused models to high-volume mass market distribution
- Demonstrated experience enabling and managing distribution channels in semiconductor or similar B2B industries
- Track record of driving operational transformation (process redesign, infrastructure build, organizational scaling)
- Experience building or evolving digital/web capabilities to support customer self-service at scale
Skills & Attributes
- Strategic mindset with ability to develop and execute multi-year transformation plans
- Strong cross-functional leadership and ability to influence without direct authority
- Operational excellence and process discipline
- Customer-centric focus; deep understanding of mass market customer needs and buying behavior
- Data-driven decision making
- Excellent communication skills; ability to present to executive leadership and translate strategy to execution teams
Education
- BS in Electrical Engineering or related field (MS or MBA a plus).
Additional Information
Renesas is an embedded semiconductor solution provider driven by its Purpose, To Make Our Lives Easier. With a global team of over 21,000 engineers and problem solvers in more than 30 countries, we offer the opportunity to work on world‑leading technology for Automotive, Industrial, Infrastructure, and IoT, shaping a safer, healthier, greener, and smarter future.
At Renesas, TAGIE is our culture, grounded in being Transparent, Agile, Global, Innovative, and Entrepreneurial. It shapes how we work, grow and deliver on our purpose together. This collaborative spirit and mindset drive our semiconductor technology to transform industries and impact millions of lives.
We believe in rewarding our employees with a competitive benefits package alongside their salary. More information will be provided during the hiring process.
Are you ready to join our team and shape the future with us?
Renesas Electronics is an equal opportunity and affirmative action employer, committed to supporting diversity and fostering a work environment free of discrimination on the basis of sex, race, religion, national origin, gender, gender identity, gender expression, age, sexual orientation, military status, veteran status, or any other basis protected by law. For more information, please read our Diversity & Inclusion Statement.
Renesas Electronics deals with dual-use technology that is subject to U.S. export controls regulations. Under these regulations it may be necessary for Renesas to obtain U.S. government export license prior to release of technology to certain persons. The decision whether or not to file or pursue an export license application is at the sole discretion of Renesas.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Renesas, 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 in Demand for This Role
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 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Renesas AI Hiring
Renesas has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Tempe, AZ, US, Austin, TX, US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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