Collaborate with our core development team to bring machine learning into production - Design and train ML models on large-scale clinical da
Agile Robots SE is an innovative company with a clear mission: bringing intelligent robotic solutions to our customers.
is based in Munich, Germany.
Wir schreiben dir, sobald eine neue geprüfte Stelle in München dazukommt. Höchstens eine Nachricht am Tag, und nur, wenn es tatsächlich etwas Neues gibt.
Abmelden mit einem Klick, jederzeit. Keine Weitergabe an Dritte.
Warum dieser Job gelistet ist
Eindeutige KI-Rollenbezeichnung im Stellentitel. Prüfverfahren ansehen
Collaborate with our core development team to bring machine learning into production - Design and train ML models on large-scale clinical datasets - Integrate AI-powered solutions (LLMs, ASR, RAG, Embedding, and more in the future) into real medical workflows - Evaluate, benchmark, and fine-tune existing models to fit clinical use cases - Work with Federated Machine Learning frameworks for privacy-preserving model training - Collaborate with leading academic and research institutions - Own the reliability, performance, and quality of AI features in production - Leverage access to high-performance computing infrastructure to build robust ML pipelines
Degree in Informatics, Computer Science, or a related field - or equivalent practical experience - Strong coding background - GitHub contributions are a plus - Hands-on experience deploying ML systems to production using PyTorch, LangChain, and Pydantic, including LLMs, ASR models, and RAG pipelines with vector databases (e.g., Qdrant). - Publications at top conferences or demonstrated cutting-edge results in applied ML (e.g., Speech Recognition or NLP) is a plus - Interest in new technologies and their application to real-world challenges - Fluency in English, German is a plus YOUR
Variety in your tasks and the opportunity to take ownership - An international, informal team structure in a motivating start-up atmosphere, allowing you to participate directly in our customer growth story and the overall success of Avelios - Ability to gain valuable hands-on experience in a startup backed by leading international VCs - Support for your personal development through continuous learning and feedback - Set your own rhythm with flexible working hours and trust-based time tracking - A virtual stock option package (VSOP) that lets you participate in Avelios’ long-term value creation - A monthly meal allowance up to 75 euros, full coverage of the Deutschlandticket, Urban Sports Club or a subsidized Wellpass membership, a bike leasing program and Corporate Benefits that give you access to discounts from a wide range of providers in various areas
Our mission at Avelios is to unlock clinical data to power seamless healthcare operations for better patient care. To do so, we have built a modular software platform that digitizes and optimizes workflows in hospitals with cutting-edge technology in a user-friendly way. With our software, we enable hospitals, doctors & nurses to provide their patients with the best possible care.
We are growing fast and want to keep expanding our team and business to fundamentally digitize healthcare for the better. We appreciate different backgrounds and see diversity as one of our strengths. THE TEAM AND ROLE As a Machine Learning Engineer (all genders), you will drive the integration of intelligent systems into real-world clinical environments.
Collaborating with our interdisciplinary development teams, your role will focus on bringing state-of-the-art models (LLMs, ASR, and more), into production and embedding them directly into Avelios software to improve everyday medical workflows. This is a unique opportunity to shape how AI transforms the way clinicians work.
Interesse an dieser Position?
Die vollständige Anzeige und das Bewerbungsformular liegen beim Arbeitgeber.
Zur Originalanzeige32 geprüfte Stellen bei 31 Arbeitgebern, 2 davon ortsunabhängig. KI in bestehende Software und Abläufe bringen: Agenten, RAG-Systeme, Schnittstellen, automatisierte Arbeitsketten.