Tondo is an Infrastructure Intelligence company specializing in transforming urban infrastructure into a secure and scalable protection layer. Using edge hardware, distributed AI sensors, and real-time analytics, Tondo enables effective monitoring, protection, and optimization of public spaces, low-altitude airspace, and sensitive perimeters. Deployed in Israel, Europe, and the United States, our technology provides real-time detection of drones, gunshots, and other threats, ensuring faster response times and stronger public safety. With privacy-focused edge AI architecture, our systems protect communities while safeguarding personal data. Tondo delivers reliable and resilient solutions designed to enhance security and trust in critical public environments.

We are looking for a skilled Python Developer with strong experience in Embedded Linux environments to join our team. The ideal candidate has a solid understanding of hardware-software integration and hands-on experience implementing edge AI models on resource-constrained devices.

You will work at the intersection of software, hardware, and machine learning, developing robust, efficient, and scalable solutions deployed directly on edge devices.

• Develop and maintain Python-based applications for Embedded Linux systems
• Design and implement software that interfaces directly with hardware components (sensors, controllers, communication modules)
• Deploy, optimize, and maintain edge AI / ML models on embedded devices
• Optimize performance, memory usage, and power consumption for constrained environments
• Work closely with hardware engineers to debug and validate system-level issues
• Integrate third-party libraries, drivers, and APIs into embedded systems
• Build and maintain CI/CD pipelines for embedded deployments (when applicable)
• Document system architecture, code, and development processes

• 3+ years of experience in Python development
• Hands-on experience with Embedded Linux (e.g., Yocto, Buildroot, or similar)
• Strong understanding of Linux internals, processes, and system-level debugging
• Experience working with hardware interfaces (UART, SPI, I2C, GPIO, etc.)
• Proven experience deploying machine learning models on edge devices
• Familiarity with frameworks such as TensorFlow Lite, PyTorch, ONNX Runtime, or similar
• Experience with cross-compilation and embedded toolchains
• Strong problem-solving skills and ability to work independently

• Experience with edge AI optimization techniques (quantization, pruning, model compression)
• Background in C/C++ for low-level or performance-critical components
• Experience with ARM-based platforms (e.g., Raspberry Pi, NVIDIA Jetson, ESP32, etc.)
• Knowledge of Docker or containerization in embedded environments
• Familiarity with real-time systems or RTOS
• Experience with wireless communication protocols (BLE, Wi-Fi, etc.)
  

• Experience in IoT systems and cloud integration
• Understanding of data pipelines from edge to cloud
• Experience with OTA (Over-The-Air) updates
• Prior work in production-grade embedded systems