Machine Learning Research Scientist

European Tech Recruit; Berlin; Hybrid; EUR 65.000 - 95.000; Vollzeit

Zusammenfassung

European Tech Recruit seeks a Machine Learning Scientist (Probabilistic Inference) in Berlin, hybrid. You will develop and evaluate amortised inference methods for high-dimensional distributions and build learning methods for large probabilistic models.

You will design experiments, collaborate with mathematicians, and translate theory into scalable Python implementations while communicating complex results to diverse stakeholders.

Qualifikationen

• PhD or advanced degree in CS/Math/ML/Statistics or related field.
• 3+ years of ML research experience with strong probabilistic inference skills.
• Track record in probabilistic inference or modelling research.
• Experience in Bayesian, sampling-based or amortised inference, causal modelling, RL, or optimal control.
• Strong Python programming and ML framework experience (PyTorch or TensorFlow).

Aufgaben

• Develop novel amortised inference methods for high-dimensional distributions.
• Develop parameter-learning and structure-learning methods for large probabilistic graphical models.
• Design rigorous evaluation strategies for probabilistic ML methods.
• Run experiments to evaluate new inference and learning approaches; interpret results to guide research.
• Collaborate with mathematicians and researchers on theoretical questions.
• Translate theory into high-quality, maintainable Python implementations.
• Communicate complex results to multidisciplinary stakeholders.

Kenntnisse

Amortised inference

Probabilistic inference

Bayesian inference

Reinforcement learning

Generative Flow Networks

Python

PyTorch

TensorFlow

Mathematical foundation

Ausbildung

PhD preferred in related field

Tools

PyTorch

TensorFlow

Python

Jobbeschreibung

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.