Applied Ml Scientist, Cheminformatics

Summary

Join Genesis Therapeutics as a critical bridge between AI research and experimental drug discovery teams. Collaborate closely with both groups, leveraging cutting-edge AI techniques to evaluate, analyze, and deploy models. Maximize the impact of our industry-leading AI platform on drug discovery programs. The ideal candidate will be comfortable in experimental and engineering contexts and skilled at communicating with stakeholders from both domains. Positions are available at various levels of seniority: Senior, Staff, and Principal. Day-to-day responsibilities require basic familiarity with experimental techniques, excellent data science skills, and demonstrated ability in applied ML.

Requirements

• Experience working with chemistry project teams in an industrial or academic drug discovery setting
• Experience with modeling and analysis of small molecule datasets
• Familiarity with common experiment types (biochemical/binding/cell-based assays, in vivo studies, etc.) and CADD workflows (docking, virtual screening, ADME prediction, etc.)
• Excellent data science skills, including exploratory data analysis and statistical methods for model comparison and evaluation
• Strong software engineering skills, including experience with Python, Numpy, and Matplotlib
• Strong cheminformatics skills, including experience with RDKit or OpenEye
• Strong applied ML background, including familiarity with deep neural networks and experience with scikit-learn and PyTorch

Responsibilities

• Work directly with project teams to assess model performance and utility, including applicability to current project needs, and collaborate with ML and engineering teams to resolve issues or add new functionality
• Assist experimental colleagues with use and interpretation of model predictions by providing context about model quality and prediction uncertainty
• Evaluate model quality by validating predictions against project data and internal or external benchmarks
• Curate internal and external datasets for model training and validation (in collaboration with experimental teams)
• Contribute to design and analysis of experiments on model changes and alternative architectures

Preferred Qualifications

• Publications in peer-reviewed journals or conferences describing ML applications in drug discovery or related areas
• PhD or equivalent in cheminformatics, computer-aided drug design, or a related field
• Experience with graph neural networks, multitask modeling, active learning, Bayesian optimization, model uncertainty, and multi-objective optimization
• Experience with implementation of ML model architectures in PyTorch
• Experience in a collaborative software engineering environment, including code reviews and pull request workflows
• Experience with SQL and database management
• Strong opinions on molecule featurization and model validation

Benefits

• Competitive compensation package that includes salary and equity
• Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees)
• 401(k) plan
• Open (unlimited) PTO policy
• Free lunches and dinners at our offices
• Paid family leave (maternity and paternity)
• Life and long- and short-term disability insurance