Lead AI Architect for Edge & Embedded Intelligence

Renesas Electronics Corp.; Plano (TX); On-site; USD 140,000 - 210,000; Full time

Job summary

Renesas Electronics Americas seeks an experienced AI Architect to define, drive, and evolve AI/ML across embedded and edge platforms. You will bridge algorithms, sensor analytics, and semiconductor tech while partnering with product teams, system architects, and strategic customers to deliver differentiated AI-enabled solutions.

You will mentor engineering teams, shape AI strategy, and accelerate edge intelligence while contributing to reference architectures, papers, and ecosystem initiatives.

Qualifications

• Advanced degree in a quantitative field.
• 10+ years in AI/ML, analytics, or intelligent embedded systems.
• Proven track record in AI architecture or senior technical leadership.

Responsibilities

• Define AI/ML architectures for embedded and edge systems.
• Translate market needs into AI solution roadmaps.
• Develop reusable AI frameworks and deployment pipelines.
• Lead evaluation and adoption of ML/DL, foundation models, and agentic-AI.
• Collaborate with product, software, and research teams to integrate AI capabilities.
• Mentor and provide technical leadership to engineering teams.
• Support strategic customer engagements as AI advisor.
• Monitor AI advances and identify differentiation opportunities.

Skills

AI architectures

ML & DL

Signal processing

Computer vision

Sensor analytics

Edge AI

Data fusion

Time-series analysis

Explainable AI

Edge computing

Education

Master's or Ph.D. in CS/AI/EE/Math/Data Science

Tools

PyTorch

TensorFlow

ONNX

Job description

Renesas Electronics Americas seeks an experienced AI Architect to define, drive, and evolve AI/ML across embedded and edge platforms. You will bridge algorithms, sensor analytics, and semiconductor tech while partnering with product teams, system architects, and strategic customers to deliver differentiated AI-enabled solutions.

You will mentor engineering teams, shape AI strategy, and accelerate edge intelligence while contributing to reference architectures, papers, and ecosystem initiatives.