Applied AI Scientist (Cheminformatics)

TL;DR

Applied AI Scientist (Cheminformatics): Building next-generation generative AI models for molecular design and property prediction with an accent on scalable, production-ready models for sequencing and diagnostics. Focus on designing state-of-the-art generative pipelines, implementing GFlowNets, and optimizing molecules for specific chemical and biological characteristics.

Location: Hybrid in Mississauga, Ontario, Canada

Salary: $35-42/Hr

Company

An independent technology consulting firm providing guidance and solutions to businesses across Information Systems, Telecom, Life Sciences, and Engineering.

What you will do

• Design and implement generative AI pipelines to create novel small-molecule candidates optimized for sequencing and diagnostic platforms.
• Develop property-guided molecular generation models and build ML models to predict molecular properties and phenotypes from 2D/3D structures.
• Deploy advanced generative architectures including Transformers, LLMs, Graph Neural Networks (GNNs), Diffusion Models, VAEs, and GFlowNets.
• Develop AI models for Computer-Aided Synthesis Planning (CASP) to ensure feasible synthetic routes.
• Apply few-shot and transfer learning to integrate public chemical databases with proprietary internal datasets.
• Collaborate with experimental chemists and R&D teams to integrate in-silico predictions into real-world research workflows.

Requirements

• PhD (completed or pursuing) or equivalent research experience in Computational Chemistry, Bioinformatics, Machine Learning, or a related technical field.
• Deep understanding of AI/ML methods applied to molecular modeling and cheminformatics.
• Hands-on experience building generative AI models for molecular design and property-guided molecule generation.
• Strong programming skills in Python and experience with PyTorch, RDKit, NumPy, and SciPy.
• Must be based in or able to work hybrid in Mississauga, Ontario, Canada.

Nice to have

• Experience with molecular foundation models or large-scale chemical datasets.
• Knowledge of protein–ligand interactions, medicinal chemistry, or chemical reaction modeling.
• Experience deploying ML models into production or scalable research pipelines.
• Familiarity with cloud computing environments (AWS, GCP, Azure).
• Publications in machine learning, cheminformatics, or computational chemistry.

Culture & Benefits

• International community with over 110 different nationalities.
• Strong internal growth culture where 70% of leaders started their careers at the entry level.
• Robust training system through an internal Academy with 250+ available modules.
• Dynamic work environment featuring regular internal events, afterworks, and team buildings.

Hiring process

• Brief introductory call to discuss motivations and fit.
• Average of 3 interviews with the line manager and team members.
• Possible case study or technical assessment depending on the candidate's profile.