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

Testen, Auswerten und Trainieren von aktuellen KI-Modellen und -Architekturen, Implementierung von ML-Pipelines, Entwicklung von Software und Frameworks, Analyse und Bearbeitung verschiedener Datenmodalitäten (Bilder, Zeitreihen, etc.), Sichtung und Auswertung relevanter Literatur und Datensätze, Erstellung und Durchführung von Präsentationen

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

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

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

Unterstützung bei der Entwicklung und Optimierung von Computer Vision Modellen und Algorithmen, Durchführung von Datensammlungen und Bildannotationen für Trainingszwecke, Mitwirkung bei der Implementierung von Machine Learning Projekten in den Bereichen Stereo Matching, Bildklassifikation und Objekterkennung, Testen und Evaluieren von Modellen und Algorithmen zur Verbesserung der Erkennungsgenauigkeit, Zusammenarbeit mit einem interdisziplinären Team aus Entwicklern und Data Scientists

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