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About This Role
Company Description
Renesas Electronics Americas (REA) is a global semiconductor leader delivering innovative embedded solutions that enable billions of connected, intelligent, and sustainable devices worldwide. We combine the resources and stability of a multi-billion-dollar global organization with the speed, collaboration, and customer focus of an entrepreneurial technology company. Our products and solutions support some of the industry's most exciting innovations across AI infrastructure, cloud and networking platforms, automotive systems, industrial automation, robotics, and IoT applications. Join Renesas and help drive the next generation of technology with the world's leading innovators.
We are seeking an experienced Key Account Manager in the California Bay Area to join Renesas' Ecosystem Partners team and lead engagement with one of our most strategic global semiconductor customers. This customer is a leading provider of networking, broadband, custom silicon, storage, and AI infrastructure solutions serving hyperscale cloud, enterprise, and communications markets. In this role, you will drive strategic ecosystem initiatives, foster technical and business partnerships, and expand Renesas content through reference designs, platform engagements, and next-generation infrastructure programs.
Job Description
About the Role
We are looking for an experienced and visionary AI Architect to define, drive, and evolve artificial intelligence and machine learning technologies across a broad range of intelligent embedded and edge computing solutions.
In this role, you will serve as a technical authority for AI and machine learning, bridging advanced algorithms, sensor analytics, edge computing, and semiconductor technologies. You will collaborate with product groups, system architects, software teams, and strategic customers to develop innovative AI-enabled solutions that deliver differentiated value across multiple market segments.
Your work will help shape the company's AI strategy, accelerate adoption of edge intelligence, and enable next-generation intelligent systems through advanced machine learning, signal processing, perception, and data analytics technologies.
Key Responsibilities
- Define and own AI/ML technology architectures spanning embedded systems, edge computing, cloud-connected systems, and intelligent sensor solutions.
- Translate customer challenges, market opportunities, and technology trends into AI solution architectures and technology roadmaps.
- Drive development of reusable AI frameworks, software architectures, inference pipelines, model-deployment methodologies, and reference implementations.
- Lead the evaluation, development, and adoption of machine learning, deep learning, foundation-model, and agentic-AI technologies.
- Define architectures for:
- Sensor intelligence
- Signal processing
- Computer vision
- Anomaly detection
- Predictive analytics
- Sensor fusion
- Autonomous decision making
- Multimodal AI systems
- Drive hardware/software partitioning across MCUs, MPUs, AI accelerators, NPUs, DSPs, FPGAs, and cloud resources.
- Define efficient deployment strategies for AI workloads on resource-constrained embedded platforms.
- Evaluate and optimize AI models for accuracy, latency, memory footprint, power consumption, and cost.
- Collaborate closely with product groups, software teams, system architects, and research partners to integrate AI capabilities into future solutions.
- Support strategic customer engagements and act as a trusted advisor on AI architecture and deployment.
- Monitor advances in AI/ML research and identify opportunities for product differentiation and innovation.
- Contribute to AI reference architectures, technical papers, application notes, patents, and ecosystem initiatives.
- Provide mentoring and technical leadership to engineering teams across the organization.
Qualifications* Master's or Ph.D. degree in Computer Science, Applied Mathematics, Artificial Intelligence, Electrical Engineering, Physics, Data Science, or a related field.
- 10+ years of experience in AI/ML, advanced analytics, signal processing, computer vision, or intelligent embedded systems.
- Proven track record as an AI Architect, Principal AI Engineer, Chief Architect, AI Research Lead, or equivalent technical leadership role.
Proven Expertise In: Machine Learning, Deep Learning, Statistical Modeling, Signal Processing, Computer Vision, Sensor Analytics, Data Fusion, Pattern Recognition, Predictive Analytics, Time-Series Analysis, Explainable AI, Edge AI
Strong Experience With: PyTorch, TensorFlow, ONNX, ML model optimization, Data pipelines, AI deployment frameworks
Strong Understanding Of: Embedded systems, Edge computing platforms, Heterogeneous compute architectures, MCUs, MPUs, NPUs, DSPs, and AI accelerators, Performance, power, and memory optimization
Additional Information
Nice to Have
- PhD in Applied Mathematics, Machine Learning, Signal Processing, Computer Vision, or related discipline
- Experience developing patented AI technologies or algorithms
- Publications in AI, machine learning, signal processing, or computer vision
- Experience with foundation models, multimodal AI, and agentic AI systems
- Experience with sensor analytics and anomaly detection
- Experience in embedded AI deployment
- Familiarity with semiconductor architectures and AI acceleration technologies
- Industry experience spanning multiple domains such as industrial automation, automotive, healthcare, robotics, aerospace, IoT, or consumer electronics
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 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
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
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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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