Sr. Staff Embedded AI Engineer

About

Renesas is seeking a Sr. Staff Embedded AI Engineer to develop advanced TinyML and embedded AI solutions targeting Renesas microcontroller and MPU platforms (RA, RL78, RX, RZ). This is a highly technical, hands‑on role focused on building cloud‑based model translation infrastructure and optimizing network inference for resource‑constrained embedded systems. You will contribute to a small team developing a service that converts trained machine learning models into efficient C/C++ implementations for deployment on microcontrollers. The ideal candidate combines strong embedded software expertise with solid machine learning fundamentals and is comfortable working across the stack – from neural network internals to low‑level performance optimization. You should be someone who contributes new ideas, challenges assumptions, and helps improve both tooling and embedded implementation quality.

Responsibilities

• Build cloud‑based model translation infrastructure.
• Optimize network inference for resource‑constrained embedded systems.
• Convert trained machine learning models into efficient C/C++ implementations for microcontroller deployment.
• Contribute new ideas, challenge assumptions, and improve tooling and embedded implementation quality.

Requirements

• BS/MS/PhD in Electrical Engineering, Computer Engineering, Computer Science, or related field.
• 6+ years of experience in embedded systems software development.
• Strong proficiency in C/C++ for embedded platforms.
• Strong proficiency in Python for tooling, automation, or ML workflows.
• Experience deploying machine learning models to resource‑constrained systems.
• Solid understanding of neural network fundamentals and internals.
• Experience with machine learning frameworks such as TensorFlow or PyTorch.
• Experience optimizing performance, memory footprint, and power consumption on embedded targets.

Qualifications

• Experience developing inference runtimes, model translation tools, or code generation systems.
• Experience with CMSIS‑NN or other embedded ML acceleration libraries.
• Experience optimizing quantized neural networks for embedded systems using SIMD/DSP acceleration.
• Familiarity with Renesas MCU/MPU platforms (RA, RL78, RX, RZ).
• Experience with real‑time systems (RTOS or bare‑metal).
• Hardware debugging experience.

Benefits

• Opportunities to launch and advance your career in technical and business roles across four Product Groups and various corporate functions.
• Ability to explore hardware and software capabilities and try new things.
• Make a real impact by developing innovative products and solutions for global customers.
• Flexible and inclusive work environment with remote work option (two days a week) and Employee Resource Groups.
• Hybrid work model: remote two days a week, in‑office Tuesday through Thursday for collaboration and learning.