Job
- Level
- Erfahren
- Ort
- Wien
- Arbeitsmodell
- Onsite
- Job Feld
- Data, Embedded
- Anstellung
- Vollzeit
- Vertragsart
- Unbefristetes Dienstverhältnis
- Gehalt
- 68.000 bis 75.000€ Brutto/Jahr
Job Zusammenfassung
In dieser Position entwickelst du KI-Modelle für Audio und multimodale Intelligenz auf Geräten, optimierst Algorithmen und setzt sie in Echtzeitsystemen um. Dabei arbeitest du eng mit verschiedenen Fachbereichen zusammen, um die Modellleistung zu maximieren.
Job Technologien
Deine Rolle im 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.
- Integrate ML inference into real-time 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.
Unsere Erwartungen an dich
Qualifikationen
- 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.
Erfahrung
- 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.
- Experience with real-time streaming inference pipelines.
- Experience with on-device ML (TinyML, quantization, pruning).
Unser Angebot
- EUR 68000-75000 per year.
Benefits
Essen & Trinken
Work-Life-Integration
Gesundheit, Fitness & Fun
Themen mit denen du dich im Job beschäftigst
Job Standorte
Das ist dein Arbeitgeber
StreamUnlimited Engineering GmbH
Wien
Unsere Software und elektronischen Lösungen für Audio Streaming sind die besten auf dem Markt. Sie werden von berühmten Audio-Marken sowie Herstellern intelligenter Heimprodukte eingesetzt.
Description
- Gründungsjahr
- 2005
- Unternehmenstyp
- Etablierte Firma
- Arbeitsmodell
- Hybrid, Onsite
- Branche
- Internet, IT, Telekom
Machine Learning Engineer
- Gehalt
- 68.000 bis 75.000€ Brutto/JahrNetto aus dem Bruttogehalt im Inserat umgerechnet.45.364 bis 49.093 € netto/Jahr
- Ort
- Wien
- Arbeitsmodell
- Onsite
- Diversität
- Für alle Personen geeignet (m/w/d)
- Nur Englisch
- Nur Englisch erforderlich
