Lead Embedded Systems Software Engineer

Join our team as a Lead Embedded Software Engineer, where you will be responsible for translating product requirements into system-level software architecture, writing production code, and leading a team of embedded engineers. This player-coach role requires a deep knowledge of embedded software development, proficiency in C/C++, and experience with Zephyr RTOS. You will collaborate with hardware, systems, and product teams, and contribute to hiring and team standards.

• Lead the full path from product requirements to working, tested firmware, translating product-level goals into system software architecture.
• Write production code, review designs, and set technical direction while guiding a team of embedded engineers to ship reliable, high-performance software.
• Make and own key technical decisions, document them clearly for the team, and identify risks early, driving solutions before they become blockers.
• Deep knowledge of at least one MCU family: STM32, Nordic nRF, or ESP32
• 7+ years of embedded software development experience in production systems
• Demonstrated ability to lead a technical team while remaining a hands-on contributor
• Experience integrating and characterizing inertial sensors (IMU, accelerometer, gyroscope)
• Proficiency with hardware debug tooling: JTAG, SWD, logic analyzers, and oscilloscopes
• Hands-on experience with Zephyr RTOS, including West build system and CMake
• Strong C/C++ skills in resource-constrained environments (bare-metal and RTOS)
• Strong skills in writing automated tests for embedded code: unit tests, mocking, HIL/SIL frameworks
• Solid understanding of wireless protocols - BLE, Thread, and Wi-Fi - including their stack implementations
• Experience designing and implementing backend communications (MQTT, CoAP, HTTP/REST, or similar)
• Solid Git workflow practices: branching strategy, code review process, and CI/CD integration
• Experience with Rust in embedded contexts
• Background in aerospace, satellite, or other safety-critical systems
• Familiarity with TinyML or edge ML frameworks (TFLite Micro, Edge Impulse, ONNX Runtime)
• Contributions to open-source embedded or RTOS projects
• Experience with model optimization techniques such as quantization and pruning
• Clear communicator. Able to explain complex technical decisions to non-specialist stakeholders