Machine Learning Engineer
Develops, optimizes, and deploys ML models for audio and multimodal intelligence on edge devices at StreamUnlimited in Vienna. Works at the intersection of ML modeling, DSP, and embedded systems, converting trained models into efficient C/C++ implementations for MCUs, DSPs, and NPUs under tight latency, memory, and power constraints.
- Compensation
- Not disclosed
- Location
- Vienna, Vienna, Austria
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 20+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- Strong proficiency in C/C++ for embedded systems
- Strong experience developing and evaluating ML models, preferably for audio, speech, or 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
Nice to have
- 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
Full description
StreamUnlimited is a passionate team of 120 music lovers and tech geeks across Vienna (HQ), Bratislava, Boston, Shenzhen, and Osaka, on a mission to make the global home streaming experience downright awesome. For over 20 years, we have delivered what we promise because we genuinely care about our people and our work. Following our acquisition by Bose in 2026, we are now empowered to do even more of what we love.
We are building the algorithms, systems, and platforms that power the next generation of audio and multimodal experiences—from embedded devices to cloudconnected products.
Our ambition is bold: to become the world’s Audio Lab — inventing, optimizing, and delivering meaningful and magical experiences anywhere sound matters.
We sit at the intersection of digital signal processing (DSP), deep learning, embedded systems, and software infrastructure, turning new ideas into realworld capabilities across a wide range of hardware and product surfaces.
Our group:
- Invents and develops audio and multimodal ML algorithms for real-world product experiences.
- Designs efficient neural networks and signal processing pipelines for embedded and on-device deployment.
- Optimizes models for resource-constrained hardware, including MCUs, DSPs, NPUs, and custom accelerators.
- Build tools, infrastructure, and reference implementations that accelerate the journey from invention prototype product.
- Learns from real-world deployment constraints to inform the next generation of algorithms, models, and platforms.
- If you love building ML systems where algorithms meet real hardware –– and you want your models to run efficiently in the real world –– this is the place.
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).