ML Research Scientist

Responsibilities

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

Requirements

• Advanced degree in Computer Science, Mathematics, or a relevant field
• Minimum of 3 years of experience in deep learning research
• Expertise in probabilistic inference
• Expertise in one or more: Bayesian inference, sampling-based approximate inference, amortized inference (variational inference or GFlowNets), parameter/structure learning in probabilistic graphical models, reinforcement learning, or optimal control
• Strong mathematical background
• Proficiency in Python and ML frameworks like PyTorch or TensorFlow
• Proven track record of contributing to high-quality research

Benefits

• Comprehensive health benefits
• Minimum of 20 days vacation per year
• Minimum 4% employer retirement savings contribution
• Generous flexible benefits
• Collaborative and inclusive work environment

About the Company

LawZero is a non-profit organization committed to advancing research and creating technical solutions that enable safe-by-design AI systems. Based in Montreal, LawZero’s research aims to build non-agentic AI that learns to understand the world through transparent and externalized probabilistic reasoning.