Offer summary

Qualifications:

Experience in drug discovery within chemistry project teams, either industrial or academic., Strong data science skills, including exploratory data analysis and statistical methods., Proficiency in software engineering with Python, Numpy, and Matplotlib., Applied ML background with familiarity in deep neural networks and tools like scikit-learn and PyTorch..

Key responsabilities:

Collaborate with project teams to assess and enhance model performance for drug discovery.

Assist experimental teams in interpreting model predictions and understanding prediction uncertainty.

Curate datasets for model training and validation in collaboration with experimental teams.

Contribute to the design and analysis of experiments related to model changes and architectures.

Job description

About the Team

We’re a tight-knit team of proven drug hunters, deep learning researchers, and software engineers united by a common mission — drive AI innovation in biochemistry, discovering and developing groundbreaking therapies for patients suffering from severe disorders.

Genesis AI team is focused on developing foundation models for drug discovery by conducting fundamental research at the intersection of machine learning, physics, and computational chemistry, as well as engineering robust software systems that enable running large scale simulations and training generative and predictive AI models designed to learn from all kinds of molecular data, leveraging our cluster with 1000s of GPUs and 10,000s of CPUs.

About the Role

This unique role serves as a critical bridge between our AI research and experimental drug discovery teams. You will collaborate closely with both groups, leveraging cutting-edge AI techniques to evaluate, analyze, and deploy models to 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. Their day to day responsibilities will require basic familiarity with experimental techniques, excellent data science skills, and demonstrated ability in applied ML.

Positions are available at various levels of seniority: Senior, Staff, and Principal.

You

willWork 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 function
alityAssist experimental colleagues with use and interpretation of model predictions by providing context about model quality and prediction uncert
aintyEvaluate model quality by validating predictions against project data and internal or external bench
marksCurate 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.

You Are

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 OpenEyeStrong applied ML background, including familiarity with deep neural networks and experience with scikit learn and PyTorch

Nice to have's

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

Compensation, Benefits, and Perks

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

Required profile