Applied AI Scientist – CheminformaticsWe are seeking a researcher‑driven builder who combines deep expertise in machine learning with a solid background in computational chemistry to help build next‑generation generative AI models capable of designing molecules with optimized properties and realistic synthesis pathways.Key ResponsibilitiesDesign and implement state‑of‑the‑art generative AI pipelines to create novel small‑molecule candidates optimized for performance within sequencing and diagnostic platforms.Develop property‑guided molecular generation models to optimize molecules for desired chemical and biological characteristics.Build machine learning models that predict molecular properties and phenotypes from 2D or 3D chemical structures, helping scientists prioritize candidates prior to synthesis.Apply cheminformatics approaches to analyze molecular datasets and identify patterns relevant to experimental performance.Design, train, and deploy advanced generative architectures, including Transformers, Large Language Models (LLMs), Graph Neural Networks (GNNs), Diffusion Models, Variational Autoencoders (VAEs), and Reinforcement Learning approaches such as GFlowNets.Develop AI models for Computer‑Aided Synthesis Planning (CASP) to ensure generated molecules have feasible synthetic routes and reaction pathways.Apply few‑shot learning and transfer learning techniques to integrate representations learned from large public chemical databases with proprietary internal datasets.Fine‑tune public molecular foundation models on internal data to improve property prediction accuracy and molecule optimization.Partner closely with experimental chemists, computational scientists,



and R&D teams to integrate in‑silico predictions into real‑world research workflows.Contribute to the development of applied AI frameworks used across the organization’s R&D pipeline.QualificationsPhD (completed or currently pursuing) or equivalent research experience in Computational Chemistry, Bioinformatics, Biophysics, Bioengineering, Computer Science, 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.Demonstrated expertise in property‑guided molecule generation.Strong programming skills in Python.Experience with machine learning and cheminformatics libraries such as PyTorch, RDKit, NumPy / SciPy, and deep learning frameworks.Nice to HaveExperience working 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) for model training and data processing.Experience collaborating with wet‑lab scientists or R&D teams in biotechnology or pharmaceutical environments.Publications in machine learning, cheminformatics, or computational chemistry.Expected Compensation Range35‑42 $/Hr. The final compensation offered will be determined based on the candidate’s level of experience, skills, and qualifications, in compliance with applicable pay transparency requirements.Canadian Experience RequirementNo Canadian work experience is required for this position. Candidates are encouraged to apply regardless of previous Canadian experience.Equal Opportunity StatementAmaris Consulting promotes equal opportunities. We are committed to bringing together people from diverse backgrounds and creating an inclusive work environment. In this regard, we welcome applications from all qualified individuals, regardless of sex, sexual orientation, race, ethnicity, beliefs, age, marital status, disability, or other characteristics.#J-18808-Ljbffr

📌 Applied Ai Scientist Cheminformatics - $35 - $42 An Hour (Toronto)
🏢 Amaris Consulting
📍 Toronto