Descripción
Who we areStreamUnlimited Engineering develops software and electronics for audio streaming, voice assistant devices, wireless smart speakers and is the global leader in this field. Our solutions are used by Premium Consumer Brands, as well as manufacturers of Sma... 1 Machine Learning Engineer (all genders) About the Role * As an Edge AI ML Engineer, you will develop, optimize, and deploy machine learning models for audio and multimodal intelligence on real devices. * You will work at the boundary of ML modeling, DSP, and embedded systems, turning research concepts into efficient, robust, production-ready algorithms. * You will collaborate closely with ML researchers, DSP experts, firmware engineers, and hardware teams to design algorithms that perform well not only in the lab, but also under real-world constraints such as latency, memory and power. * This role is ideal for an ML engineer who enjoys model development, experimentation, and algorithm design, while also caring deeply about whether those models can run efficiently on edge hardware.In This Role, You Will * Identify opportunities for new DSP/ML algorithms by deeply understanding device constraints, sensor characteristics and hardware capabilities (MCU, DSP, NPU). * Develop audio and multimodal ML models for embedded and edge AI applications. * Design, train, evaluate, and iterate on models for real-world sensing and interaction use cases. * Prototype novel approaches that push what's possible in lowlatency, ondevic audio and multimodal processing.Embedded / OnDevice Engineering * Develop software for RTOS environments (e.g., FreeRTOS) and deploy models to device runtimes and hardware accelerators (DSP, NPU, MCU). * Convert trained ML models into efficient embedded implementations (C/C++, quantization, fixedpoint inference). * Optimize runtime performance: memory footprint, SRAM usage, latency, and power consumption. * Integrate ML inference into realtime firmware pipelines. * Design endtoend embedded AI