Senior Staff Application Technology Architect – Edge Artificial Intelligence & Machine Learning

Renesas Electronics•Austin, TX

25d

About The Position

Renesas is seeking an experienced Edge AI Architect to define, drive, and evolve AI and machine learning technologies across intelligent embedded and edge-computing solutions. The role serves as a technical authority for AI and machine learning, connecting advanced algorithms, sensor analytics, edge computing, and semiconductor technologies. It works with product groups, system architects, software teams, research partners, and strategic customers to develop AI-enabled solutions across multiple markets, shape the company's AI strategy, and advance adoption of edge intelligence. The architect owns AI/ML technology architectures across embedded systems, edge computing, cloud-connected systems, and intelligent sensors. The role develops reusable frameworks, software architectures, inference pipelines, model-deployment methods, and reference implementations; evaluates and adopts machine-learning technologies; and designs efficient AI deployment strategies for constrained platforms. It also advises customers, monitors research advances, contributes to technical and ecosystem materials, and provides technical leadership and mentoring.

Requirements

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.
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, and Edge AI.
Strong experience with PyTorch, TensorFlow, ONNX, ML model optimization, data pipelines, and AI deployment frameworks.
Strong understanding of embedded systems, edge-computing platforms, heterogeneous compute architectures, MCUs, MPUs, NPUs, DSPs, AI accelerators, and performance, power, and memory optimization.

Nice To Haves

Ph.D. in Applied Mathematics, Machine Learning, Signal Processing, Computer Vision, or a 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 across multiple domains such as industrial automation, automotive, healthcare, robotics, aerospace, IoT, or consumer electronics.

Responsibilities

Define and own AI/ML technology architectures for 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 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, and multimodal AI systems.
Drive hardware/software partitioning across MCUs, MPUs, AI accelerators, NPUs, DSPs, FPGAs, and cloud resources.
Define efficient strategies for deploying AI workloads on resource-constrained embedded platforms.
Evaluate and optimize AI models for accuracy, latency, memory footprint, power consumption, and cost.
Collaborate with product groups, software teams, system architects, and research partners to integrate AI capabilities into future solutions.
Support strategic customer engagements and advise 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.