ML Research Scientist (probabilistic inference)

LawZero; Berlin; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

LawZero in Berlin is seeking an ML Research Scientist to advance probabilistic inference methods, with a focus on amortized inference, translating theory into practical Python implementations.

You will develop amortized inference for high-dimensional distributions, collaborate with mathematicians, and evaluate new approaches for safe-by-design AI, contributing to research that informs responsible AI systems.

Qualifikationen

• Advanced degree in CS or Mathematics; PhD preferred.
• Minimum 3 years of deep learning research experience.
• Expertise in probabilistic inference (Bayesian, sampling-based, amortized).

Aufgaben

• Develop amortized inference methods for high-dimensional distributions.
• Build parameter- and structure-learning methods for probabilistic graphical models.
• Design evaluation strategies for probabilistic inference methods.
• Collaborate with mathematicians on theory related to learning and inference.
• Translate theoretical proposals into Python implementations.
• Analyze and interpret experimental results to steer research directions.
• Communicate complex findings clearly to diverse stakeholders.

Kenntnisse

Deep learning research

Probabilistic inference

Python programming

PyTorch

TensorFlow

Mathematics

Communication skills

Analytical thinking

Ausbildung

Advanced degree in Computer Science or Mathematics

PhD preferred

Tools

PyTorch

TensorFlow

Jobbeschreibung

ML Research Scientist (probabilistic inference)

Berlin; Montreal

We are seeking a Machine Learning (ML) Research Scientist to join our team working on a novel AI safety research agenda. In this role, you will develop and evaluate probabilistic inference methods, with a focus on amortized inference, translating theoretical insights into practical implementations.

Key responsibilities

• Develop amortized inference methods suitable for high-dimensional discrete and continuous distributions.
• Develop parameter- and structure-learning methods for large probabilistic graphical models that benefit from amortized probabilistic inference.
• Design evaluation strategies for methods that rely on probabilistic inference.
• Collaborate with mathematicians on theory related to learning and inference in probabilistic models.
• Translate theoretical proposals into high quality implementations in a programming language such as Python.
• Analyze and interpret experimental results to steer future research directions.
• Communicate complex findings effectively to various stakeholders.

Skills and qualifications

• Advanced degree in a relevant field (e.g., Computer Science, Mathematics). A PhD is preferred but not required if the candidate demonstrates exceptional abilities.
• A minimum of 3 years of experience in deep learning research.
• Expertise in probabilistic inference is required, in addition to expertise in one or more of the following:
  • Bayesian inference
  • Sampling-based approximate inference methods
  • Amortized inference methods (including variational inference methods or Generative Flow Networks)
  • Parameter- and/or structure-learning in probabilistic graphical models (including causal models)
  • Reinforcement learning
  • Optimal control
• Strong background in mathematics.
• Proven experience in developing and implementing machine learning models.
• Proficiency in programming languages such as Python, and experience with ML frameworks like PyTorch or TensorFlow.
• Excellent analytical and problem-solving skills, with a demonstrated ability to think critically about complex systems.
• Strong communication skills, both written and verbal, with the ability to explain complex ideas to diverse audiences.
• Track record of contributing to high-quality research in probabilistic inference or related fields.
• Ability to work collaboratively in a team environment while also being self-motivated and independent.

About LawZero

LawZero is a non-profit organization committed to advancing research and creating technical solutions that enable safe-by-design AI systems. Its scientific direction is based on new research and methods proposed by Professor Yoshua Bengio, the most cited AI researcher in the world. Based in Montreal, LawZero’s research aims to build non-agentic AI that learns primarily to understand the world rather than to act in it, giving truthful answers to questions based on transparent and externalized probabilistic reasoning. Such AI systems could be used to accelerate scientific discovery, to provide oversight for agentic AI systems, and to advance the understanding of AI risks and how to avoid them. LawZero believes that AI should be cultivated as a global public good—developed and used safely towards human flourishing. For more information, visit www.lawzero.org

You belong here

At LawZero, diversity is important to us. We value a work environment that is fair, open and respectful of differences. We welcome applications from highly qualified individuals interested in working towards our mission in a respectful, inclusive and collaborative setting.

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