Senior Embedded Applications Engineer, Tiny ML Lab

Job description

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.

Qualifications

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 experience in physical system fabrication, 3D printing, electrical assembly, PCB bring-up, and low-level debugging with oscilloscopes, logic analyzers, and JTAG/SWD.

Embedded Firmware

Strong C/C++ programming for constrained microcontrollers, bare-metal or RTOS (FreeRTOS, Zephyr), DMA buffer management, and low-level drivers (SPI, I2C, UART, CAN).

Signal Processing & On-Device ML

Solid understanding of digital signal processing (DSP) for time-series/audio data and practical experience deploying quantized ML models directly onto microcontrollers.