We’re looking for a Cheminformatician

VANCOUVER, BC (OR REMOTE) / 2+ YRS PROFESSIONAL EXPERIENCE

Variational AI is radically accelerating the development of promising drug candidates by integrating chemistry and pharmacology expertise with the state-of-the-art in machine learning. Traditional approaches to small molecule drug discovery require over ten years and two billion dollars, and their reliance on trial-and-error calls out for better predictive and generative models. The current industry standard has progressed little beyond shallow ML techniques such as random forests and support vector machines, largely due to the difficulty of integrating world-class machine learning research with traditional chemistry and pharmacology approaches. Variational AI is building a generative foundation model for molecular structure and properties from the ground up. Over the past five years, we have been advancing the state-of-the-art, and delivering projects to customers including Merck, Rakovina Therapeutics, and ImmVue Therapeutics.

We are searching for a cheminformatician to join us in our quest to radically accelerate the development of new drugs through machine learning excellence. You will help identify, clean, prepare, and test datasets; develop ligand- and structure-based featurizations; apply traditional cheminformatics techniques; and evaluate new targets. In this process, you will have the opportunity to build your skills by collaborating with our team of accomplished ML scientists, computational chemists, and medicinal chemists. No knowledge of machine learning is required for this role, but you should possess a strong interest in learning about this promising technology, coupled with hands-on drug discovery experience.

Here is the background we’re looking for:

• M.S. (Ph.D. preferred) in chemistry or a related discipline, with experience in cheminformatics and data science.
• Two or more years’ experience working in small molecule drug discovery.
• Demonstrated skill with chemical informatics including the aggregation, curation, and preparation of large experimental datasets.
• Experience developing and applying cheminformatics techniques to improve hit identification and hit-to-lead processes.
• Strong programming skills in Python and experience with cheminformatics libraries and data analysis tools, including RDKit, Matplotlib, pandas, and seaborn. SQL experience preferred.
• Experience with structure- and ligand-based modeling of small molecule binding affinity and ADMET, including QSAR, molecular docking, and molecular dynamics/ABFE/RBFE is preferred.
• Intellectual curiosity and drive to excel.

We are an equal opportunity employer and enthusiastically welcome applications from women, BIPOC, LGBTQ+, and members of underrepresented communities and groups. Compensation is a competitive mix of cash and options. We prioritize expertise and passion over where you decide to live and work. Please send resumes/CVs to info@variational.ai.