We are looking for a Senior Machine Learning Engineer to join ML Core, the team that builds the shared ML models behind SoundCloud’s recomme
Konzeption und Implementierung skalierbarer ML-Pipelines für Training, Evaluation, Deployment und Optimierung Aufbau moderner Embedding- und
XALT builds AI solutions that actually work – not just as proof-of-concepts, but in production.
At Helsing we deliver AI-based capabilities and the enabling foundation that allow machines to perceive and assist human decision-making.
At Helsing we deliver AI-based capabilities and the enabling foundation that allow machines to perceive and assist human decision-making.
Wir schreiben dir, sobald eine neue geprüfte Stelle dazukommt. Höchstens eine Nachricht am Tag, und nur, wenn es tatsächlich etwas Neues gibt.
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VOQUZ ist ein weltweit agierender IT-Konzern mit über 300 Kolleginnen und Kollegen in 8 Ländern auf 3 Kontinenten, der Großunternehmen und d
Help us use technology to make a big green dent in the universe!
AI Engineer - FDE (Forward Deployed Engineer) Req ID: CSQ427R334 We welcome remote applicants located near our offices.
NVIDIA has been redefining computer graphics and accelerated computing for three decades.
provides an opportunity to build and develop strong practical data science skills while contributing to measurable business outcomes.
We are looking for a talented graduate to join our AI development team and learn practical AI/ML and data engineering skills.
Integriert Deep-Learning-Modelle in DFT-Software, schreibt effiziente GPU-Implementierungen dafür und pflegt Open-Source-Releases.
Overview The mission of the team is to enable highly accurate and scalable predictions of the electronic structure of molecules and material
Du entwickelst in abwechslungsreichen Projekten Machine-Learning- und GenAI-Lösungen für unsere Kunden, von LLM-Anwendungen und RAG-Systemen bis zu KI-Agenten.
Du entwickelst in abwechslungsreichen Projekten Machine-Learning- und GenAI-Lösungen für unsere Kunden, von LLM-Anwendungen und RAG-Systemen bis zu KI-Agenten.
Entwickelt KI-Emulatoren für biomolekulare Strukturen und Wechselwirkungen, schreibt Forschungscode und koordiniert Datenerhebung und Trainingsläufe.
KI für Bilder und Punktwolken entwickeln: Du entwickelst Anwendungen zur Auswertung von Bilddaten, Punktwolken und 3D-Daten, beispielsweise
YOU ARE As a hands on AI Engineer, you will be at the heart of designing and building the components that make up advanced AI systems poweri
Introduction HeiGIT (Heidelberg Institute for Geoinformation Technology) As a nonprofit research institute, HeiGIT (Heidelberg Institute for
will combine hands-on technical exposure with scientific curiosity and business relevance: from extracting insights from client interactions
HOLYWATER TECH — це технологічна компанія, яка створює розважальні продукти на перетині людської творчості та штучного інтелекту.
Company team.blue is an ecosystem of 60+ successful brands working together across 22 European countries to provide its 3.3 million SMB cust
Forschung an Multi-Tier-Optical-Circuit-Switching-Netzen für KI-Rechenzentren: Modelle bauen, Traffic-Placement optimieren, Algorithmen simulieren und publizieren.
If you re excited by the idea of building technologies that go beyond today s AI accelerators and helping define the future of computing, we
im Team You will support the ECHT team in its mission of transforming traditional materials research into a fully digitalised, data-driven p
Warum dieser Job gelistet ist
Eindeutige KI-Rollenbezeichnung im Stellentitel. Prüfverfahren ansehen
3+ years building ML systems in production, with end-to-end ownership of at least one ranking, retrieval or recommendation model. - Strong deep learning skills in PyTorch (or similar frameworks), and a good grasp of recommendation and retrieval fundamentals (embeddings, two-tower models, learning to rank, multi-objective ranking) and their trade-offs. - Solid experience with evaluation and experimentation for ranking systems, including biases in implicit feedback such as clicks and plays. - Strong Python; comfortable with large-scale data processing (e.g., Spark/BigQuery) and with getting models to production on a cloud platform (GCP preferred). - M.Sc. in a quantitative field or equivalent experience.
Fine-tuning or serving transformer-based embedding/ranking models. - Applying LLMs or foundation models to recommendations (content understanding, semantic IDs, generative retrieval, feature generation). - Audio or music ML, or multimodal representations. - Contextual bandits, RL or causal methods for long-term value. - Publications at RecSys, KDD, SIGIR, WSDM, NeurIPS or similar. - Experience with ANN/vector search and GPU training or serving at scale. We encourage you to apply even if you don t meet every qualification. Research suggests that some applicants may doubt their candidacy, so please submit an application if you are interested.
We are looking for a Senior Machine Learning Engineer to join ML Core, the team that builds the shared ML models behind SoundCloud’s recommendations, search and content understanding. New sounds start on SoundCloud. Our job is to connect artists with the communities that will care about their music, and help listeners discover the gems and emerging scenes they’d never find on their own.
Recommendations are our largest area, but you’ll work across problems that share the same foundations: representing users, tracks and creators, and ranking them well. We’re also in the middle of modernizing our stack, moving from classical approaches to deep learning on GPU, and you’ll play a big part in that. Key Responsibilities: - Design, train and ship models for recommendations (candidate generation, user/item modeling, ranking) and related problems in search and content understanding. - Lead the move of key models from classical approaches (e.g., word2vec-style embeddings) to PyTorch-based models trained and served on GPU. - Adapt, fine-tune and evaluate open-source embedding and ranking models for our catalog and use cases. - Own evaluation end to end: offline metrics, A/B experiments, and making sure offline gains show up online. - Work with ML, data and backend engineers to get models into production and keep them healthy, and help set modeling standards across the team.
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