Senior - Firmware Engineer

Senior - Firmware Engineer

At Gradera, we are seeking a highly experienced Senior Firmware Engineer with strong expertise in NXP-based hardware platforms, Yocto-based Linux, and RTOS. The ideal candidate will design and develop firmware solutions that enable real-time data acquisition, processing, and seamless integration with cloud-based Machine Learning (ML) platforms for advanced analytics and training.

This role requires hands-on experience in low-level programming, embedded C# development, and building scalable pipelines to feed device data into cloud ML systems.

Key Responsibilities:

Design, develop, and maintain firmware for NXP SoCs/MCUs (i.MX, LPC, Kinetis, etc.) using Yocto Linux and RTOS platforms

Build and customize Linux images using Yocto (BitBake, recipes, layers)

Develop real-time firmware components with deterministic performance using RTOS

Implement embedded applications using C/C++ and C# (.NET on embedded targets)

Design and optimize data acquisition pipelines from sensors/devices

Ensure reliable and secure transmission of device data to cloud platforms (AWS / Azure / GCP)

Collaborate with data engineering and ML teams to:

Structure data for ML training

Enable streaming/batch ingestion pipelines

Integrate communication protocols:

MQTT, HTTP/HTTPS, WebSockets, CAN, UART, SPI, I2C

Optimize firmware for performance, memory, and power consumption

Debug and troubleshoot firmware issues across hardware and software layers

Implement OTA (Over-the-Air) firmware updates

Ensure security best practices (encryption, secure boot, device authentication)

Required Skills & Qualifications:

6+ years of experience in embedded firmware development

Strong experience with Yocto Project (custom layers, recipes, kernel configuration)

Solid understanding of RTOS concepts:

Task scheduling

Interrupt handling

Synchronization (mutex, semaphores)

Real-time constraints

Proficiency in:

C / C++ (mandatory)

C# (.NET Core / .NET nanoFramework / .NET IoT on embedded systems)

Experience working with Linux kernel, drivers, and device tree

Hands-on with hardware interfaces (GPIO, I2C, SPI, UART)

Experience with edge-to-cloud integration

Knowledge of data streaming pipelines for ML systems

Familiarity with containerization (Docker) is a plus

Cloud & ML Integration (Must Have):

Experience sending device data to cloud platforms:

Azure IoT Hub / AWS IoT Core / GCP IoT

Understanding of ML data requirements:

Data preprocessing

Feature extraction

Data labeling pipelines

Exposure to ML workflows:

Data ingestion → storage → training → inference

Knowledge of tools like:

Kafka / Pub-Sub / Kinesis (preferred)

Nice to Have:

Experience with Edge AI / TinyML

Knowledge of CI/CD for firmware

Familiarity with Cybersecurity standards (TLS, secure provisioning)

Experience with industrial protocols (Modbus, OPC-UA)