Machine Learning Engineer (all genders)

Who we are

StreamUnlimited 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 systems (sensor ¿ preprocessing ¿ model ¿ postprocessing).

Platform & Performance Engineering

• Architect reusable embedded ML platform components usable across multiple hardware targets.
• Profile and optimize performance on MCUs, DSP cores, NPUs, and custom accelerators.
• Work with crosscompilation toolchains, CMakebased builds, and modular codebases.
• Integrate with ondevice ML runtimes (e.g., TFLite Micro, ExecuTorch, custom interpreters).
• Provide actionable feedback on model architecture for deployment efficiency and realtime behavior.

Required Qualifications

• Strong proficiency in C/C++ for embedded systems.
• Strong experience developing and evaluating machine learning models, preferably for audio, speech, or other time-series sensor data.
• Experience optimizing ML models for edge, embedded, or resource-constrained environments.
• Proficiency in Python for model development, experimentation, evaluation, and tooling.

Preferred Qualifications

• Master's or Ph.D. in Computer Science, Electrical Engineering, Machine Learning or related field.
• Experience with ML compilers/frameworks such as MLIR, Glow, ExecuTorch.
• Experience with real-time streaming inference pipelines.
• Knowledge of acoustics and classical audio DSP.
• Experience with on-device ML (TinyML, quantization, pruning).
• Publication track record in ML, DSP, systems, or embedded AI.

You Might Thrive Here If You...

• Love building systems where algorithms meet real hardware.
• Enjoy profiling and optimizing code under tight compute and memory constraints.
• Take ownership - from prototype to hardware bringup to production.
• Want your work to directly shape the next generation of audiodriven experiences.

Apply now: https://ams.at/su/1ejYz

Das Mindestentgelt für die Stelle als Machine Learning Engineer (all genders) beträgt 68.000,00 EUR brutto pro Jahr auf Basis Vollzeitbeschäftigung. Bereitschaft zur Überzahlung.