RT Embedded and AI Developer

5 days ago

Yehud Monosson Center District, Israel Real Time Group - Software Solutions Full time

Real Time Group, LTD
is looking for a skilled
Real-Time Embedded Developer with expertise in Artificial Intelligence (AI)
to join our dynamic engineering team. The ideal candidate will be responsible for designing, developing, and optimizing high-performance, low-latency firmware and software for
resource-constrained embedded systems
. This role requires a unique blend of deep real-time operating system (RTOS) knowledge, low-level programming proficiency, and practical experience in deploying and optimizing AI/Machine Learning (ML) models on edge devices.

Job Responsibilities:

  • Develop and maintain
    efficient, reliable, and testable real-time firmware in
    C/C++
    for various microcontrollers (MCUs) and microprocessors (MPUs).
  • Design, implement, and optimize
    RTOS
    applications for determinism, low latency, and efficient resource utilization..
  • Perform hardware-software integration
    and debugging using tools like oscilloscopes, logic analyzers, and in-circuit emulators/debuggers.
  • Develop and implement
    communication protocols (e.g., SPI, I2C, UART, Ethernet, Wi-Fi, Bluetooth LE).
  • Contribute to the entire software development lifecycle
    , including requirements definition, design, coding, testing, and deployment.
  • Collaborate
    with hardware engineers to define specifications, select components, and bring up new hardware platforms.

Job Requirements:

Embedded Systems Expertise:

  • Bachelor's or Master's degree
    in Electrical Engineering, Computer Science, or a related technical field.
  • 5+ years
    of professional experience in
    embedded software development
    .
  • Expert-level proficiency in
    C and C++
    for embedded systems.
  • Deep practical experience with
    RTOS concepts
    (task scheduling, memory management, synchronization primitives, interrupt handling).
  • Proven experience with
    bare-metal programming
    and understanding of hardware-software interfaces, including register-level programming.
  • Strong debugging skills for challenging real-time issues, including race conditions and priority inversion.

AI and Edge Computing Expertise:

  • Solid understanding of
    Machine Learning (ML) fundamentals
    and common model architectures (e.g., CNNs, RNNs/LSTMs, transformers).
  • Practical experience in
    quantization, pruning, and model compression
    techniques to reduce model size and improve inference speed on edge devices.
  • Experience with
    ML deployment frameworks
    such as
    TensorFlow Lite/TFLite-Micro
    or similar tools for on-device inference.
  • Familiarity with
    Embedded Linux
    or other operating systems for higher-end edge devices is a plus.
  • Ability to
    profile and benchmark
    AI model performance (latency, memory, power) on target hardware.

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