Deep Learning Scientist Intern, Cheminformatics and Virtual Screening

As an Applied Deep Learning Scientist Intern, Cheminformatics & Virtual Screening, you will join a research and development team focused on infrastructure development and collaborations with industry and academic partners.

This position provides the opportunity to research, implement, productize, and deliver deep learning algorithms for applications to life sciences and drug discovery.

Prototype and build deep learning algorithms for small molecule data and AI-assisted virtual screening workflows Collaborate with multiple AI infrastructure and research teams

MS or PhD Degree in a quantitative field such as Statistics, Physics, Computational Biology, Computer Science, Mathematics (or a related field) and within one year of graduation

Expertise in deep learning and machine learning

Strong experience with Python libraries for data science analysis (e.g. NumPy, Pandas, Matplotlib) and deep learning (PyTorch or TensorFlow) Expertise in cheminformatics, virtual screening, and processing of biochemical data You should display strong communication skills, be organized and self-motivated, and play well with others (be a phenomenal teammate)

Experience with open source development Background with C/C++, CUDA, docker

Experience with cheminformatics and related tools, such as RDKit, KNIME, AutoDock Vina, and Maestro

Experience with diffusion models, equivariant models, and/or transformer-based NLP models Relevant publication history and/or conference attendance

Trillion is an equal opportunity employer. Applicants are considered for positions without regard to race, religion, gender, native origin, age, disability, or any other category protected by applicable federal, state, or local law.