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Machine Learning Research Scientist
Veröffentlicht am
- Arbeitsort
- 10115 Berlin, Berlin, Deutschland
- Gehaltsschätzung
- 75.000–120.000 € brutto/Jahr · KI-Schätzung von Salary Quick anhand von Jobtitel und Ort (Stand 2026-10-03). Arbeitszeit und individuelle Konditionen können den Betrag verändern. Keine Gehaltszusage.
Stellenbeschreibung
Machine Learning Scientist (Probabilistic Inference)- AI Safety Research Non-Profit - Berlin (Hybrid)
We are seeking a Machine Learning (ML) Research Scientist specialising in probabilistic inference to join a highly technical research team working at the forefront of machine learning and probabilistic modelling. In this role, you will develop and evaluate advanced probabilistic inference methods, with a particular focus on amortised inference, translating theoretical insights into practical, scalable implementations.
Research & Algorithm Development:
- Develop novel amortised inference methods suitable for high-dimensional discrete and continuous probability distributions.
- Develop parameter-learning and structure-learning methods for large-scale probabilistic graphical models.
- Design rigorous evaluation strategies for ML methods relying on probabilistic inference.
- Design and execute experiments to evaluate new inference and learning approaches, analysing results to drive future research directions.
- Collaborate closely with mathematicians and researchers on theoretical questions.
- Translate theoretical concepts and research proposals into high-quality, maintainable implementations using Python.
- Clearly communicate complex technical findings to multidisciplinary stakeholders.
Requirements:
- Advanced degree (PhD preferred) in Computer Science, Mathematics, Machine Learning, Statistics, or a related discipline.
- Minimum of 3 years of experience in deep learning or machine learning research, with strong expertise in probabilistic inference and a solid mathematical foundation.
- Track record of contributing to high-quality research in probabilistic inference, probabilistic modelling, or related fields.
- Experience in one or more areas: Bayesian inference, sampling-based approximate inference, amortised inference (variational inference, Generative Flow Networks), causal modelling, reinforcement learning, or optimal control.
- Strong Python programming skills and experience with ML frameworks like PyTorch or TensorFlow.