> Markdown version of [/jobs/ext/3854506-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/3854506-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** StreamUnlimited Engineering GmbH - **Location:** Wien, Austria - **Salary:** €68,000.0 - €75,000.0 - **Contract:** Permanent contract - **Skills:** Algorithm Design, Artificial Neural Networks, C++ (Programming Language), Firmware, FreeRTOS, Python (Programming Language), Machine Learning, Real-Time Operating Systems, Azure Machine Learning, Signal Processing, Software Engineering, Data Streaming, Discretization, Information Technology, Build Tools, Embedded AI, Hardware Acceleration, Machine Learning Operations, MLIR (Multi-Level Intermediate Representation) - **Published:** October 22, 2026 - **Apply:** https://www.adzuna.at/details/5935475944 ## About the Role * 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. ## Description * 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). * 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.