Applied AI Scientist, Cheminformatics

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 Client 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 Client 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 Client 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 Clients 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.





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