Sr Staff Application Engineer

Renesas Electronics•Austin, TX

About The Position

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.

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.
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

Nice To Haves

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

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.