Jobs

Description

Work Location: This is an onsite role within the Mississauga Campus. Candidate will be required to work onsite at least 3 days every week.

Applied AI Scientist, Cheminformatics (Contractor - 4 months)

• We are seeking an exceptional AI/ML scientist with a strong background in computational chemistry and a deep interest in molecular foundation models and targeted molecule generation.
• Ideal candidates are motivated builders who can take ideas from AI research papers and translate them into robust, scalable in-silico models that predict molecular performance.

Qualifications

• PhD, pursuing a PhD degree (currently enrolled student) or equivalent advanced research experience in Computational Chemistry, Biophysics, Bioengineering, Computer Science, or a related technical field.
• Deep understanding of AI/ML methods specifically applied to molecular modeling and cheminformatics.
• Hands-on experience building and deploying generative AI architectures, specifically Transformers, Large Language Models (LLMs), Graph Neural Networks (GNNs), Diffusion models, Variational Autoencoders (VAEs), GFlowNets Reinforcement Learning Leraning (RL).
• Proven expertise and hands-on experience specifically in Property-Guided Molecule Generation.
• Proficiency in Python and experience writing clean, modular, and testable code using standard ML and cheminformatics libraries (e.g., PyTorch, RDKit).

Responsibilities

• Design and implement state-of-the-art generative AI pipelines to design novel small-molecule candidates optimized for specific performance metrics within our sequencing platforms.
• Design, train, and deploy advanced generative architectures for Computer-Aided Synthesis Planning (CASP), ensuring proposed molecules have highly feasible reaction pathways.
• Build automated machine learning models capable of predicting molecular performance phenotypes from 2D chemical structures, helping chemists prioritize or eliminate candidates prior to synthesis.
• Apply advanced few-shot learning techniques to combine molecular representations learned from massive public databases with client’s proprietary, high-quality datasets.
• Fine-tune public models on proprietary data for property prediction and to optimize relevant performance metrics.
• Work closely with experimental chemists and internal stakeholders to integrate in-silico predictions into applied AI frameworks used across our R&D pipeline.

Benefits

• A dedicated 4-months, full-time (40 hours per week) professional contract.
• Project commencement scheduled for June 1st, 2026.
• Competitive Compensation.
• Ownership of meaningful, business-critical applied AI projects that directly impact commercial sequencing instruments.
• Opportunity to work with experienced AI engineers, chemists, and ML practitioners in the biotechnology industry.

AI Disclosure: This recruitment process will not use automated or AI-enabled tools to assist with application screening and scheduling. All hiring decisions are made by qualified human reviewers.