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Job Description
Renesas is seeking a Manager, Content Strategy \& AI Visibility to lead how our products, technologies, and solutions are planned, structured, and surfaced across every place customers look for answers, including Renesas.com, partner and distribution ecosystems, AI\-powered answer engines, and traditional search. As AI increasingly influences how engineers research, evaluate, and select semiconductor solutions, this role will ensure Renesas content is comprehensive, authoritative, and optimized to be discovered, understood, cited, and trusted wherever customers seek information.
This is both a strategic and people\-leadership role. You will define and execute Renesas' global content strategy across web experiences, product content, blogs, videos, and other digital channels, ensuring customers can easily access the information they need throughout their journey. You will establish the vision, roadmap, governance model, and content standards that improve content quality, consistency, discoverability, and business impact across the Renesas global digital ecosystem.
The ideal candidate combines expertise in content strategy, SEO/GEO, content operations, and digital experience optimization. You understand that visibility is no longer defined solely by rankings and blue links, but by how effectively content is structured, connected, and surfaced across search engines, AI systems, partner ecosystems, and customer touchpoints.
Key Responsibilities
- AI visibility \& discoverability strategy: Own the GEO and SEO strategy and roadmap, positioning Renesas as an authoritative, trusted source across AI\-driven and traditional search experiences. Drive answer\-first content architecture, entity optimization, and structured data initiatives that increase discoverability, strengthen authority signals, and ensure content can be accurately surfaced, cited, and trusted by search engines and AI systems.
- Content strategy: Define and evolve Renesas content strategy across product, technical, and marketing content, ensuring the information customers need is available throughout the buying and design journey. Establish content priorities, identify gaps, guide editorial roadmaps, and align content investments with business objectives, customer needs, and market opportunities.
- Content operations \& governance: Establish scalable content intake, review, and governance processes that ensure content requirements, metadata, taxonomy, SEO/GEO considerations, and supporting assets are complete before work is handed off to web production and development teams. Drive consistency, quality, and operational efficiency across the content lifecycle.
- Team leadership: Lead and develop a team of content strategists responsible for content planning, governance, intake processes, and optimization initiatives. Establish priorities, drive execution, and foster a customer\-centric, data\-driven approach to content excellence.
- Information architecture \& taxonomy: Define and evolve content architecture, taxonomy, metadata standards, and organizational frameworks that improve findability, scalability, consistency, and discoverability across web, search, AI, and partner ecosystems.
- Onsite search: Partner with the product team that owns onsite search to drive the content strategy behind it, monitoring onsite search behavior, identifying gaps, and maintaining the database of synonyms and rules to continually improve onsite SERP relevance and performance.
- Technical GEO/SEO: Work hand\-in\-hand with the product and development teams to implement and monitor technical GEO/SEO, establishing ongoing monitoring to catch issues early.
- Best practices \& enablement: Serve as the internal authority on GEO and SEO best practices, educating marketers and both technical and non\-technical stakeholders so new site features and content adhere to machine\-readable, citation\-worthy standards.
- Agency management: Manage our China search agency and other content, SEO, and digital experience partners, holding them accountable to clear goals, regional best practices, and measurable outcomes.
- Measurement \& insights: Define the metrics that matter across content performance, customer engagement, discoverability, and AI visibility. Establish reporting frameworks that demonstrate business impact, identify optimization opportunities, and translate insights into actionable recommendations.
- Market awareness: Monitor platform changes, competitive activity, emerging tools, and shifts in search behavior to recommend strategic adjustments, bringing an experimentation mindset that tests new approaches and doubles down on what works.
- Cross\-functional partnership: Act as the connective tissue across content, web, engineering, product, and executive stakeholders to communicate priorities, create shared touchpoints, and drive execution.
Qualifications* 7\+ years of experience in content strategy, SEO, AI\-driven discovery, digital experience, content operations, or related disciplines, including experience leading teams and cross\-functional initiatives.
- Expertise in how LLMs and answer engines discover, evaluate, and cite content, and how AI\-powered search is changing brand visibility and content performance.
- Experience developing and scaling content programs across web, product, technical, and marketing channels, including editorial planning, content standards, and quality management.
- Experience establishing content intake processes, governance models, and workflows that enable efficient content operations at scale.
- Experience influencing cross\-functional stakeholders and driving alignment across marketing, product, engineering, and web teams.
- Familiarity with onsite search and relevance tuning, monitoring search behavior, and maintaining synonyms and rules.
- Working knowledge of technical SEO: structured data, schema markup, crawl efficiency, and site architecture at scale.
- Strong analytical skills and the ability to transform data into compelling stories that drive informed business decisions.
- Bachelor's degree in marketing, business, engineering, data science, communications, or related field.
- Experience in the semiconductor industry is a plus.
Additional Information
Austin, TX USA and San Jose, CA USA are other locations for consideration.
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. 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.
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
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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