Sr. Embedded AI Engineer

Job Title:

Sr. Staff Embedded AI Engineer Job Description

This role focuses on designing and delivering advanced TinyML and embedded AI solutions for microcontroller and MPU platforms, including RA, RL78, RX, and RZ families. You will build and enhance cloud-based model translation infrastructure that converts trained machine learning models into efficient C/C++ implementations suitable for resource‑constrained embedded systems. Working in a highly technical, hands‑on capacity, you will optimize network inference for performance, memory, and power, while contributing ideas that improve both the tooling and the quality of embedded implementations.

The position is ideal for an engineer who enjoys working across the full stack, from neural network internals to low‑level embedded optimization, and who thrives in an applied, product‑focused environment.

• Design, implement, and maintain TinyML and embedded AI solutions targeting microcontroller and MPU platforms such as RA, RL78, RX, and RZ.
• Develop and enhance a cloud‑based model translation service that converts trained machine learning models into efficient C/C++ code for deployment on embedded devices.
• Optimize neural network inference on resource‑constrained systems, focusing on performance, memory footprint, and power consumption.
• Work across the stack from neural network internals and model architecture to low‑level embedded software and hardware‑specific optimizations.
• Integrate machine learning frameworks such as Tensor Flow and PyTorch into the model translation and deployment pipeline for tiny ML solutions.
• Collaborate with a small, globally distributed team to design, build, and support cloud‑based tools that enable customers to upload data, train models, and deploy them to microcontrollers.
• Apply strong troubleshooting and debugging skills to diagnose and resolve issues in embedded AI deployments, including performance bottlenecks and functional defects.
• Contribute new ideas, challenge assumptions, and propose improvements to tooling, workflows, and embedded implementation practices.
• Manage and contribute to large‑scale software projects, ensuring maintainable, robust, and well‑documented code for both embedded and cloud components.
• Work closely with customer‑facing teams to support the deployment of AI models in real‑world applications, focusing on reliability and scalability for production use.
• Use Python to build tooling and automation that streamline machine learning workflows, model conversion, and deployment processes.
• Apply a strong mathematical foundation, including signal processing and computer science concepts, to design and refine models suitable for tiny ML and embedded AI.

• 6+ years of experience in embedded systems software development.
• Strong proficiency in C and C++ for embedded platforms.
• Strong proficiency in Python for tooling, automation, or machine learning workflows.
• Practical experience deploying machine learning models to resource‑constrained systems.
• Solid understanding of neural network fundamentals and internals, including model architecture for tiny ML solutions.
• Experience with machine learning frameworks such as Tensor Flow or PyTorch.
• Experience optimizing performance, memory footprint, and power consumption on embedded targets.
• Strong foundation in embedded engineering using C and C++.
• Robust mathematical background, including signal processing and core computer science concepts.
• Experience with large‑scale software management, including version control, modular design, and maintainable codebases.
• Excellent troubleshooting and debugging abilities in embedded and AI‑related environments.
• Ability to apply practical problem‑solving skills to real‑world engineering challenges rather than focusing solely on theoretical research.
• Experience with AI, machine learning, artificial intelligence, and embedded systems in the context of tiny ML and IoT applications.

• BS, MS, or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
• Experience working with IoT devices and ecosystems.
• Background in tiny ML solutions, including understanding of…