ML Research Scientist (probabilistic inference)

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.

What we offer

• The opportunity to contribute to a unique mission with a major impact.
• Comprehensive health benefits.
• A minimum of 20 days vacation per year upon start.
• A minimum retirement savings employer contribution of 4%.
• Generous flexible benefits designed to contribute to your well-being.
• A team of passionate experts in their field.
• A collaborative and inclusive work environment with offices in the heart of Little Italy, in the trendy Mile-Ex district, close to public transportation.

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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