10 Machine Learning Engineer Jobs

Design, develop, and maintain features using Machine Learning Models, NLP, Information Retrieval and other relevant methodologies., Deliver key data and insights into leading software platforms used by large enterprises via API., Source, process, and standardize data from multiple sources, ensuring data quality., Train, optimize, and evaluate various models to enhance accuracy and effectiveness., Deploy and maintain models on AWS, particularly with SageMaker, while managing production pipelines., Develop and explore innovative methods for modeling multilingual, semi-structured, and hierarchical data.

Create and optimise machine learning models in Python to address practical business challenges, ensuring high performance and accuracy, Architect, implement, and manage robust data analysis pipelines to efficiently process and analyze large datasets, enhancing data accessibility and usability, Engage in impactful projects such as time series analysis for demand forecasting, building recommendation systems, and customer behavior analytics to drive actionable insights and business value, Propose and implement innovative ideas for new use cases in collaboration with cross-functional teams, fostering creativity and driving advancements in AI applications

Build and maintain data processing pipelines and storage, Ensure high performance and cost-efficiency when training and executing machine learning models, Support in automating manual processes, Support in maintaining high data quality

Programmierung von Software für praktische Messaufgaben, Arbeiten mit industrieller Hardware und Nutzung von Prototyping-Plattformen (Arduino/RaspberryPi/…), Verwendung und Entwicklung von KI-Algorithmen zur Auswertung von Messdaten, Einbindung von Kameras/Sensoren/Motoren über APIs/SDKs in individuelle Programme sowie industrielle Bildverarbeitung, Nutzung externer DLLs und Software-Bibliotheken zur Auswertung der Messdaten

Make GPU training scripts faster by profiling bottlenecks and fixing them, Improve speed + add features to visualization tools used by machine learners, Understand all the technology which we use for serving ML models live and re-use them for internal tools in the machine learning team, Implement efficient AI production systems in cooperation with our software developers, Use our internal APIs to gather and transform data and optimally prepare it for ML training, Support our medical team with tooling to efficiently guide the evaluation of annotators and make the annotation process more efficient, Make GPU training robust when training across several servers, You will be directly reporting to the CTO

Explore the latest literature on state-of-the-art machine learning concepts related to time-series prediction, digital twin modeling, and energy optimization algorithms., Collaborate with our ML team to develop prototypes for forecasting heating demand and PV production using state-of-the-art ML models, and evaluate different approaches., Assist in creating state-of-the-art data-driven models for heating systems., Support the training of RL agents to optimize the operational control of heat pumps for optimal efficiency., Contribute to the design and implementation of innovative algorithms that improve energy management and efficiency., Work closely with energy engineers, ML engineers, and data scientists to develop innovative features for our smart home energy management system., Effectively present findings and insights in a concise and understandable manner, ensuring clarity for all stakeholders.

Design, train, evaluate, and deploy deep learning models in speech recognition and NLP, Engineer large self-supervised trainings, Implement fast inference for mobile devices and hosted environments, Communicate with customers to gather feedback and understand their challenges, Translate user insights into product decisions

Unterstützung des Teams bei der Entwicklung und Implementierung innovativer ML-basierter Lösungen, Einbindung in die Entwicklung und Umsetzung von Machine Learning Use Cases und Prototypen in einem agilen, iterativen Umfeld, Eigenständige (Themen-)Recherchen zur proaktiven Projektbearbeitung, Weiterentwicklung technischer Fähigkeiten und Beteiligung am technischen Kompetenzaufbau innerhalb der Science Group

Technische Orientierung für ein kleines Team von Softwareentwicklern bieten, Verantwortung für die Planung und Durchführung spannender Projekte übernehmen, Gestaltung der technischen Architektur der Projekte, Optimierung der AWS-Infrastruktur und effizientes Cloud-Management, Datengetriebene Ansätze vorantreiben und KI-Lösungen entwickeln

Entwicklung von Optimierungsprogrammen für stochastische Prozesse, Arbeit mit probabilistischen maschinellen Lernmodellen von SOTA, Arbeit mit SOTA-Optimierungsalgorithmen, Arbeit an realen Optimierungsproblemen in der Fertigung