Machine Learning Researcher

BEPC has an open position for a Machine Learning and Computational Chemistry Scientist in South San Francisco, CA!! Contract W2 Period: 12-month contract with possibilities for extensions Pay & Benefits: $77-$83/hr. + Medical, Dental, Vision and Life Insurance The client is seeking a highly motivated Machine Learning Scientist with domain expertise in mass-spectrometry (MS) data to join Machine Learning Drug discovery (MLDD) within the Client Company's Research and Early Development department. The successful candidate will drive research and engineering efforts to develop MS data platforms that enable drug discovery research within the department. This role involves extensive collaboration with computational and experimental scientists and researchers across the department to deploy and deliver machine learning and engineering solutions for small-molecule drug discovery. Implement machine learning and computational chemistry-based methods to enhance mass-spectrometry workflows, focusing on small molecule drug discovery, ensuring high accuracy and efficiency in analytical processes. Closely collaborate with other scientists and researchers within and outside MLDD to identify platform development and automation opportunities for analytical workflows that facilitate drug discovery research. Build and scale the mass-spectrometry and analytical chemistry platform by deploying novel machine learning and computational algorithms in collaboration with experimental teams that meet the research needs of the broader department and the Client Company's community. Contribute to and drive publications, present results at internal and external scientific conferences, and help make code and workflows open source. Desired Qualifications: MS or PhD degree in the physical sciences (e.g. Chemistry, Physics, Chemical Engineering) or quantitative field (e.g. Computer Science, Machine Learning) or equivalent industry research experience. 3-5 years of applicable experience. Record of scientific excellence as evidenced by at least one publication in a scientific journal or conference. Fluent in Python and experience with scientific software development for analytical chemistry (e.g. RDKit, MS data suite including, but not limited to, Proteowizard, Sirius, GNPS, XCMS, Asari). Demonstrated experience with modern Python frameworks for deep learning like PyTorch. Experience working with biochemical or biophysical datasets including graph, sequence, and structure-based data. Public portfolio of projects available on GitHub/GitLab.