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Company Description
Renesas is seeking an experienced, strategic leader to build and scale our product management organization for the High\-Performance AI and Compute (HPAC) product line.
- 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 own the complete product lifecycle and roadmap while building and leading a team of product managers at various experience levels.
This newly created director role is a pivotal opportunity to shape the future of power solutions in the world's fastest\-growing computing segments. You'll drive market leadership and revenue growth while building a best\-in\-class product organization.
Job Description
Team Leadership \& Development
- Build and Hire: Develop, retain and recruit talented product managers with diverse experience levels. Establish hiring criteria and interview processes aligned with organizational needs.
- Drive Accountability: Define roles, responsibilities, and success metrics for each PM. 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.
Product Strategy \& Portfolio Management
- Own the Roadmap: Define and communicate the product strategy, portfolio vision, and roadmap for the HPAC product line. Align with market demands, emerging trends, and business objectives.
- Identify Opportunities: Stay abreast of technology trends in cloud infrastructure and compute markets; identify new market segments, emerging applications, and growth opportunities; develop recommendations for portfolio expansion.
- Lead Product Definition: Guide the team in developing market requirement documents, business cases, and competitive analyses for new product concepts; ensure strategic alignment and resource prioritization.
- Manage the Pipeline: Monitor the health of the new product development funnel. Establish and track KPIs that indicate portfolio health, market traction, development progress, and revenue potential.
- Manage Portfolio Economics: Define and manage pricing strategies, cost structures, and margin targets for assigned products. Optimize portfolio profitability.
Operational Excellence \& Process Development
- Scale Product Management: Establish product management processes, standards, and best practices designed to support mass\-market operations and organizational growth.
- Develop Systems \& Frameworks: Create templates, processes, and decision\-making frameworks for product launches, go\-to\-market planning, pricing strategy, and customer engagement.
- Enable the Team: Establish processes, methods, and training programs that enable product managers to work more effectively and efficiently at scale.
- Cross\-Functional Alignment: Ensure full alignment with engineering, marketing and sales. Establish clear communication channels, handoff points, and governance structures.
- Continuous Improvement: Regularly review and refine processes based on team feedback and market outcomes. Drive data\-driven decision\-making across the organization.
- Drive the develop of best\-in\-class product documentation, including customer presentations, training material, product erratas, application notes, etc. Ensure compelling product collaterals and materials are developed.
Cross\-Functional Leadership
- Executive Engagement: Present product strategy, portfolio health, market opportunities, and financial projections to senior leadership. Ensure executive alignment with HPAC product plans.
- Global Operations: Work effectively with distributed teams across multiple global offices. Drive cross\-cultural collaboration and alignment.
Qualifications
- 10\+ years in product management, product marketing, or related roles.
- Hands\-on experience managing and launching products in data center, server, telecom, networking, or computing markets.
- Understanding of power management applications and products.
- Track record of driving revenue growth and market adoption.
- Strategic thinking with strong analytical, problem\-solving and decision making skills.
- Communication that translates complex concepts clearly for technical and executive audiences.
- Excellent interpersonal skills – ability to influence across teams, geographies, and cultures. Ability to be personable and drive process changes. Experience with driving change that resulted in positive outcomes. Ability to collaborate and be effective in a fast\-paced environment.
- Change leadership – experience driving organizational improvements with measurable outcomes.
- Ownership mindset – high energy, self\-motivated, and results\-oriented.
- 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
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.
Renesas AI Hiring
Renesas has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Dallas, TX, US, Austin, TX, US, Plano, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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