Senior Embedded Applications Engineer, Tiny ML Lab

Renesas Electronics•Columbia, MD

5d•Onsite

About The Position

The Renesas AIoT Center of Excellence in Columbia, MD 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. This is a physical lab execution and embedded hardware role.

Requirements

B.S. or Master’s in Electrical Engineering, Computer Engineering, Applied Physics, or a related technical discipline.
2–3+ years of hands-on career experience in embedded software, physical hardware prototyping, digital signal processing, and real-time edge computing.
Practical experience in physical system fabrication, 3D printing, electrical assembly, PCB bring-up, and low-level debugging with oscilloscopes, logic analyzers, and JTAG/SWD.
Strong C/C++ programming for constrained microcontrollers, bare-metal or RTOS (FreeRTOS, Zephyr), DMA buffer management, and low-level drivers (SPI, I2C, UART, CAN).
Solid understanding of digital signal processing (DSP) for time-series/audio data and practical experience deploying quantized ML models directly onto microcontrollers.

Nice To Haves

Experience building physical prototype rigs
Experience writing embedded C/C++
Experience running FFTs on raw accelerometer/acoustic data
Experience debugging SPI/I2C signals with an oscilloscope
Experience running TinyML on bare-metal silicon

Responsibilities

Hands-on assembly of prototype rigs, sensor arrays, 3D-printed mounts, and microelectronic setups to capture real-world physical data.
Instrument physical systems to capture, clean, and preprocess high-frequency time-series datasets (acoustic, vibration, electrical, motor current).
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.
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).
Work directly with customers and internal product teams to ingest raw hardware telemetry, debug edge firmware issues, and demonstrate working hardware solutions.
Lead junior engineers on lab tasks, document engineering best practices, and contribute technical leadership across cross-functional teams.