Machine Learning Scientist

Key Responsibilities

• Develop and implement advanced machine learning models for RNA biology, systems biology, and structural biology to solve frontier challenges in drug discovery.
• Collaborate with cross-functional teams (e.g., ML engineering, target discovery, and experimental biology) to drive research projects that identify novel drug targets and preclinical candidates.
• Design and execute computational and experimental studies to validate and improve model predictions.
• Stay informed about the latest advancements in machine learning and computational biology, and apply them to real-world challenges.
• Share research findings through presentations, publications, and technical discussions.

Basic Qualifications

• PhD in Machine Learning, Computational Biology, Bioinformatics, Computer Science, or a related technical field (MSc with significant experience also considered).
• Extensive experience in designing, training, debugging, and evaluating machine learning models using frameworks like PyTorch, TensorFlow, or JAX.
• Strong foundation in mathematics and statistics, including linear algebra, probability, and optimization.
• Excellent scientific writing and communication skills.

Preferred Qualifications

• Experience in computational biology, genomics, or drug discovery.
• Familiarity with RNA biology, structural biology, or systems biology.
• Proven track record of publishing in top-tier conferences or journals.
• Experience developing machine learning models for production, particularly in drug design.
• Proficiency with cloud computing platforms (e.g., AWS, GCP) or distributed computing frameworks.

What we offer

• A collaborative and innovative environment at the frontier of computational biology, machine learning, and drug discovery. 
• Highly competitive compensation, including meaningful stock ownership.
• Comprehensive benefits - including health, vision, and dental coverage for employees and families, employee and family assistance program. 
• Flexible work environment - including flexible hours, extended long weekends, holiday shutdown, unlimited personal days.
• Maternity and parental leave top-up coverage, as well as new parent paid time off. 
• Focus on learning and growth for all employees - learning and development budget & lunch and learns.
• Facilities located in the heart of Toronto - the epicenter of machine learning and AI research and development, and in Kendall Square, Cambridge, Mass. - a global center of biotechnology and life sciences.