Embedded Applications Engineer, Tiny ML Lab

Build Physical Rigs. Collect Real-World Sensor Data. Deploy TinyML on Silicon. About the Role & The Reality AI Lab The Renesas AIoT Center of Excellence in Columbia, MD (formerly Reality AI) is seeking a hands-on Senior Edge AI Applications Engineer to build and deploy edge AI solutions that integrate machine learning with real-world hardware systems. In this role, you will work directly in our physical lab facility, executing proof-of-concept (PoC) hardware builds, assembling custom sensor setups, and deploying low-power ML models onto microcontrollers. You will operate at the exact intersection of small-scale physical fabrication, digital signal processing (DSP), C/C++ embedded firmware, and microcontroller-level TinyML deployment. You will execute non-visual sensing solutions - instrumenting physical hardware setups (industrial motors, automotive systems, consumer devices), collecting high-frequency time-series sensor data, building custom DSP pipelines, and optimizing tiny machine learning models to run on Renesas silicon. Are you an Embedded Engineer or Applied Physicist who thrives in a physical lab environment building custom sensor rigs, soldering prototype boards, and squeezing machine learning models onto microcontrollers? ATTENTION APPLICANTS: READ BEFORE APPLYING • This is a physical lab execution and embedded hardware role. • DO NOT APPLY if your background is strictly in Cloud AI, Data Science, Generative AI, LLMs, LangChain, or Web Backend APIs. • DO APPLY if you have 2–3+ years of experience building physical prototype rigs, writing embedded C/C++, running FFTs on raw accelerometer/acoustic data, debugging SPI/I2C signals with an oscilloscope, and running TinyML on bare-metal silicon. Key Responsibilities • Physical Prototyping & Fabrication: Hands-on assembly of prototype rigs, sensor arrays, 3D-printed mounts, and microelectronic setups to capture real-world physical data. • High-Frequency Sensor Data Engineering: Instrument physical systems to capture, clean, and preprocess high-frequency time-series datasets (acoustic, vibration, electrical, motor current). • DSP & TinyML Deployment: Build DSP feature extraction pipelines (FFTs, spectral analysis, filtering) and deploy optimized, quantized TinyML models onto microcontrollers (ARM Cortex-M, Renesas RA/RX/RL78) using TFLite Micro, CMSIS-NN, or eIQ. • Embedded Firmware Development: Write real-time C/C++ firmware, bare-metal or RTOS drivers (FreeRTOS, Zephyr), DMA buffer management, and low-level peripheral communication (SPI, I2C, UART, CAN). • Customer & Cross-BU Collaboration: Work directly with customers and internal product teams to ingest raw hardware telemetry, debug edge firmware issues, and demonstrate working hardware solutions. • Technical Leadership & Mentorship: Lead junior engineers on lab tasks, document engineering best practices, and contribute technical leadership across cross-functional teams. Why Join Renesas? • No SCIF / No Clearance: Enjoy complex signal processing and hardware challenges without defense contractor bureaucracy or classified workspace restrictions. • Commercial Product Impact: What you build in our lab gets integrated into Renesas silicon and deployed into millions of industrial, automotive, and consumer devices globally. • Startup Autonomy + Global Backing: Small-team environment (under 50 people in Columbia) backed by one of the world's premier semiconductor manufacturers. • Education: B.S. or Master’s in Electrical Engineering, Computer Engineering, Applied Physics, or a related technical discipline. • Career Experience: 2–3+ years of hands-on career experience in embedded software, physical hardware prototyping, digital signal processing, and real-time edge computing. • Hardware & Lab Skills: Practical expe