Embedded Firmware Engineer

3 days ago

Lahore, Punjab, Pakistan 10xengineers Full time

Experience Required: Minimum 5 years
Location: Lahore , on-site
Job Type: Full-time

We are looking for a talented and experienced Embedded Firmware Engineer to join our team working on next-generation medical devices. The ideal candidate will have hands-on experience with Nordic Semiconductor (nRF) SoCs, Zephyr RTOS, and Bluetooth Low Energy (BLE) communication. A strong understanding of embedded security, firmware testing, and real-time system design is required.

Key Responsibilities:

Design, develop, and debug embedded firmware for Nordic nRF52/nRF53/nRF54 series using Zephyr RTOS

Develop and maintain BLE-based communication protocols (GATT, services, characteristics, MTU optimization, bonding/pairing, etc.)

Integrate sensors (e.g., accelerometer, temperature) and manage low-power data acquisition.

Implement secure firmware practices including secure bootloaders, key storage, firmware signing and encryption.

Write and execute unit tests and system-level validation tests

Use tools like nRF Connect SDK, nRF Programmer, J-Link, Wireshark, and logic analyzers for debugging and development

Apply machine learning algorithms on embedded platforms.

Requirements:

Bachelor's or Master's degree in Electrical/Electronics/Computer Engineering or related field

At least 5 years of hands-on experience in embedded firmware development

Strong experience with nRF SoCs (preferably RF52832/nRF52840/nRF5340/nRF54L15)

Proficient in C/C++, RTOS concepts, and low-level peripheral handling (SPI, I2C, UART, GPIO, ADC)

Experience working with Zephyr RTOS

Solid understanding of BLE protocol stack and development of custom BLE services.

Familiar with embedded security practices, firmware signing, over-the-air (OTA) updates, and secure storage.

Experience with firmware version control and CI/CD pipelines (e.g., Git or similar)

Familiarity with unit testing frameworks.

Exposure to DFU/OTA, USB, or bootloader development.

Experience in implementing lightweight machine learning models on embedded systems.

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