Applied AI Scientist, Cheminformatics (Contractor - 4 months)

Duration: 4 months (possible extension)

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

Description:

Applied AI Scientist, Cheminformatics (Contractor - 4 months)

• Advances in AI, data, and computational sciences are transforming molecular design and development. Client is leveraging these technologies to accelerate R&D, utilizing data and novel computational models to drive impact across our diagnostics and sequencing platforms.
• The "Gen-AI for SBX Chemistry" initiative is a strategic effort to harness the transformative power of generative AI to assist our scientists in exploring novel molecular structures and reducing design-to-test turnaround times.
• 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.