Additionalinformation

Additional Information

The expected annual pay range for this position is $84,000 - $105,000. This position is also eligible for bonus opportunities and commission pay. Please note that the final offer amount will be dependent on geographic location, applicable experience, and skillset of the candidate.

Renesas offers a full range of elective benefits including medical, health savings account (with applicable medical plan), dental, vision, health and/or dependent care flexible spending accounts, pre-tax commuter benefits, life insurance, AD&D, and pet insurance. In addition to elective benefit options, benefited employees receive company-paid life insurance and AD&D, LTD, short term medical benefits as well as paid sick time, paid holidays, and accrued paid vacation. New employees will attend a detailed benefit orientation to learn more about our many benefits and resources.

Renesas is an embedded semiconductor solution provider driven by its Purpose, To Make Our Lives Easier. With a global team of over 21,000 engineers and problem solvers in more than 30 countries, we offer the opportunity to work on world‑leading technology for Automotive, Industrial, Infrastructure, and IoT, shaping a safer, healthier, greener, and smarter future.

At Renesas, TAGIE is our culture, grounded in being Transparent, Agile, Global, Innovative, and Entrepreneurial. It shapes how we work, grow and deliver on our purpose together. This collaborative spirit and mindset drive our semiconductor technology to transform industries and impact millions of lives.

We believe in rewarding our employees with a competitive benefits package alongside their salary. More information will be provided during the hiring process.

Are you ready to join our team and shape the future with us?

Renesas Electronics is an equal opportunity and affirmative action employer, committed to supporting diversity and fostering a work environment free of discrimination on the basis of sex, race, religion, national origin, gender, gender identity, gender expression, age, sexual orientation, military status, veteran status, or any other basis protected by law. For more information, please read our Diversity & Inclusion Statement.

Renesas Electronics deals with dual-use technology that is subject to U.S. export controls regulations. Under these regulations it may be necessary for Renesas to obtain U.S. government export license prior to release of technology to certain persons. The decision whether or not to file or pursue an export license application is at the sole discretion of Renesas.

Companydescription

Company Description

Renesas is one of the top global semiconductor companies in the world. We strive to develop a safer, healthier, greener, and smarter world, and our goal is to make every endpoint intelligent by offering product solutions in the automotive, industrial, infrastructure and IoT markets. Our robust product portfolio includes world leading MCUs, SoCs, Analog and power products, plus Winning Combination solutions that curate these complementary products. We are a key supplier to the world’s leading manufacturers of electronics you rely on every day; you may not see our products, but they are all around you. Renesas employs roughly 21,000 people in more than 30 countries worldwide. As a global team, our employees actively embody the Renesas Culture, our guiding principles based on five key elements: Transparent, Agile, Global, Innovative, and Entrepreneurial. Renesas believes in, and has a commitment to, diversity and inclusion, with initiatives and a leadership team dedicated to its resources and values. At Renesas, we want to build a sustainable future where technology helps make our lives easier. Join us and build your future by being part of what’s next in electronics and the world.

Jobdescription

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.

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