Job
- Level
- Experienced
- Location
- Vienna
- Working Model
- Onsite
- Job Field
- Data, Embedded
- Employment Type
- Full Time
- Contract Type
- Permanent employment
- Salary
- 68.000 to 75.000€ gross/year
Job Summary
In this role, you will develop AI models for audio and multimodal intelligence on devices, optimize algorithms, and implement them in real-time systems while collaborating with various teams to maximize model performance.
Job Technologies
Your role in the team
- 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.
- 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 low-latency, on-device audio and multimodal processing.
- 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, fixed-point inference).
- Optimize runtime performance: memory footprint, SRAM usage, latency, and power consumption.
- Integrieren Sie ML-Inferenz in Echtzeit-Firmware-Pipelines.
- Design end-to-end embedded AI systems (sensor preprocessing, model, postprocessing).
- 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 cross-compilation toolchains, CMake-based builds, and modular codebases.
- Integrate with on-device ML runtimes (e.g., TFLite Micro, ExecuTorch, custom interpreters).
- Provide actionable feedback on model architecture for deployment efficiency and real-time behavior.
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Our expectations of you
Qualifications
- Strong proficiency in C/C++ for embedded systems.
- Proficiency in Python for model development, experimentation, evaluation, and tooling.
- Master's or Ph.D. in Computer Science, Electrical Engineering, Machine Learning or related field.
- Knowledge of acoustics and classical audio DSP.
- Publication track record in ML, DSP, systems, or embedded AI.
Experience
- 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.
- Experience with ML compilers/frameworks such as MLIR, Glow, ExecuTorch.
- Erfahrung mit Echtzeit-Streaming-Inferenzpipelines.
- Experience with on-device ML (TinyML, quantization, pruning).
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What we offer
- EUR 68,000-75,000 per year.
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Benefits
Food & Drink
Work-Life-Integration
Health, Fitness & Fun
Topics You Will Work On
Job Locations
About Your Employer
StreamUnlimited Engineering GmbH
Wien
StreamUnlimited is the leading company when it comes to Audio Streaming software and electronics. Our solutions are used by renowned Audio Brands, as well as manufacturers of Smart Home products. We develop cutting-edge technology that allows you to experience your music like never before
Description
- Founding Year
- 2005
- Company Type
- Established Company
- Working Model
- Hybrid, Onsite
- Industry
- Internet, IT, Telecommunication
Machine Learning Engineer
- Salary
- 68.000 to 75.000€ gross/yearEstimated net salary based on the gross salary in the job ad.45.364 to 49.093 € net/year
- Location
- Vienna
- Working Model
- Onsite
- Diversity
- Open for all genders
- English Only
- English only required
