Software Engineer Embedded AI

Develop firmware for a deeply embedded AI platform operating under strict latency, memory, compute, and energy constraints. Design, implement, and maintain the embedded AI software stack including optimization for performance, latency, and energy efficiency. Collaborate with AI and DSP experts to analyze and deploy DNN algorithms in hearing-device applications. Tailor AI algorithms to the embedded platform and its constraints (e.g., quantization, memory, compute and power). Implement prototypes, bring them to production-ready features and integrate them into our system in collaboration with other engineering teams. Ensure high software quality through clean-code practices, code reviews, testing, documentation, maintenance, and root-cause analysis. Continuously improve development methodologies, tools, design patterns, and best practices for efficient ML/DNN development and deployment.

At Sonova, we envision a world where everyone can enjoy the delight of hearing. This vision inspires us and fuels our commitment to developing innovative solutions that improve hearing health and human connection - from personal audio devices and wireless communication systems to hearing aids and cochlear implants. We're dedicated to providing outstanding customer experiences through our global audiological care services, ensuring that everyone has the opportunity to engage fully with the world around them. Guided by a culture of continuous improvement that fosters resilience and self-motivation, our team is united by a shared commitment to excellence and a deep sense of pride in our work, each of us playing a vital role in creating meaningful change. Here you’ll find a diverse range of opportunities that span both consumer and medical solutions and the freedom to shape your career while making an impact on the lives of others. Join us in our mission to create a more connected world, where every voice is heard and every story matters.

• Degree in Computer Science, Electrical Engineering, or a related technical field

• Strong hands-on experience with embedded C++, Python, and object-oriented programming

• Experience developing and deploying neural networks on embedded systems, including familiarity with deployment frameworks such as TensorFlow Lite, ONNX, TVM, or ExecuTorch

• Knowledge of embedded compilers, micro-NPUs, and system-level simulation

• Experience with CUDA is an advantage

• Knowledge of audio or digital signal processing is an advantage

• Experience in bringing features to products is an advantage

• Fluent English, with strong written and verbal communication skills