Sr. Staff Embedded Applications Engineer, TinyML 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 Staff Edge AI Applications Engineer to lead their physical Edge AI Applications Lab. This role involves heading the physical lab facility, directing R&D and proof-of-concept (PoC) hardware builds, managing project scheduling, and mentoring junior engineers. The position operates at the intersection of small-scale physical fabrication, digital signal processing (DSP), C/C++ embedded firmware, and microcontroller-level TinyML deployment. The engineer will lead the end-to-end execution of non-visual sensing solutions, including instrumenting physical hardware setups, collecting high-frequency time-series sensor data, building custom DSP pipelines, and optimizing tiny machine learning models for Renesas silicon. This is a physical lab leadership and embedded hardware role, not suitable for candidates focused solely on Cloud AI, Data Science, Generative AI, LLMs, LangChain, or Web Backend APIs.

Requirements

M.S. or Ph.D. in Electrical Engineering, Computer Engineering, Applied Physics, or a related technical discipline. (BSEE with extensive, demonstrable lab leadership experience will also be evaluated).
7–12+ years of hands-on experience in embedded software, physical hardware prototyping, digital signal processing, and real-time edge computing.
Proven expertise 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).
Deep understanding of digital signal processing (DSP) for time-series/audio data and practical experience deploying quantized ML models directly onto microcontrollers.

Responsibilities

Manage physical lab facilities, equipment, part procurement, and project scheduling for customer PoCs, internal R&D, and cross-BU hardware integrations.
Lead 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).
Design 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.
Work directly with customers and internal product teams to ingest raw hardware telemetry, debug edge firmware issues, and demonstrate live hardware solutions.
Lead, mentor, and guide junior lab engineers in firmware debugging, signal processing, and physical prototype fabrication.