Sr. Embedded AI Engineer

Job Description

• 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 Tensor Flow or PyTorch.
• Experience optimizing performance, memory footprint, and power consumption on embedded targets.

• 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.

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.