Machine Learning Researcher (Probabilistic Inference)

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 probabilistic inference methods
• Collaborate with mathematicians on theory related to learning and inference
• Translate theoretical propositions into high-quality Python implementations
• Analyze and interpret experimental results to guide future research
• Communicate complex results to various stakeholders

Requirements

• Advanced degree in Computer Science, Mathematics, or a related field
• Minimum 3 years of experience in deep learning research
• Expertise in probabilistic inference (Bayesian inference, sampling-based methods, or amortized inference)
• Strong mathematical knowledge
• Proven experience developing and implementing machine learning models
• Proficiency in Python and ML tools like PyTorch or TensorFlow
• Strong analytical and problem-solving skills
• Excellent written and oral communication skills

Preferred Qualifications

• PhD in a relevant field
• Expertise in Generative Flow Networks, Reinforcement Learning, or Optimal Control
• Experience with causal models

Benefits

• Comprehensive health benefits
• Minimum of 20 vacation days upon hiring
• Employer pension savings contribution of at least 4%
• Generous and flexible benefits designed for well-being
• Collaborative and inclusive work environment

About the Company

LoiZéro is a non-profit organization dedicated to advancing research and developing technical solutions for safe AI systems. Based in Montreal, LoiZéro conducts research to design non-agentic AI systems that learn to understand the world through transparent probabilistic reasoning, inspired by the work of Professor Yoshua Bengio.