Overview In this AI Architect role, you define and evolve AI/ML across embedded and edge systems, guiding architectures that span sensors, edge devices, and cloud connectivity. You will collaborate with product teams and strategic customers to craft AI-enabled solutions that differentiate Renesas' offerings. The position shapes the AI strategy, accelerates edge intelligence, and drives impactful, scalable intelligent systems. Join a mission-driven team delivering advanced AI at the edge for diverse markets.
Compensation / Benefits- competitive benefits package
Responsibilities- Define AI/ML architectures for embedded, edge, and cloud-connected systems
- Translate customer challenges and market trends into AI roadmaps and reference architectures
- Develop reusable AI frameworks, inference pipelines, deployment methodologies, and reference implementations
- Lead evaluation and adoption of ML/DL, foundation-model, and agentic-AI technologies
- Design architectures for sensor intelligence, signal processing, computer vision, anomaly detection, and predictive analytics
- Plan hardware/software partitioning across MCUs, MPUs, NPUs, DSPs, FPGAs, and cloud resources
- Create efficient deployment strategies for AI on resource-constrained embedded platforms
- Optimize models for accuracy, latency, memory, power, and cost
- Collaborate across product groups, software teams, and partners to integrate AI capabilities
- Provide mentoring and technical leadership to engineering teams
- Monitor AI/ML advances and identify opportunities for differentiation and innovation
- Contribute to AI reference architectures, papers, notes, patents, and ecosystem initiatives
- Support strategic customer engagements as a trusted AI advisor
Key requirements- 10+ years of AI/ML, analytics, signal processing, CV, or intelligent embedded systems experience
- Proven leadership in AI architecture or related senior roles
- Strong expertise in ML/DL, statistical modeling, signal processing, and edge AI
- Proficiency with PyTorch, TensorFlow, ONNX, ML model optimization, and deployment frameworks
- Solid understanding of embedded systems, edge computing, heterogeneous compute architectures (MCUs/MPUs/NPUs/DSPs/AI accelerators)
- Experience optimizing performance, power, and memory in constrained environments
- strategic thinking
- cross-functional collaboration
- technical mentoring
- AI/ML architectures
- edge AI
- sensor analytics
