Insilico Medicine develops software that leverages generative models, reinforcement learning (RL), and other modern machine learning techniques for the generation of new molecular structures with specific properties. Insilico Medicine also develops software for the generation of synthetic biological data, target identification, and the prediction of clinical trials outcomes. The company integrates two business models providing AI-powered drug discovery services and software through its Pharma.AI platform (www.insilico.com/platform/) and developing its pipeline of preclinical programs. The preclinical program is the result of pursuing novel drug targets and novel molecules discovered through its platforms. Insilico Medicine has also published over 100 peer-reviewed papers and has applied for over 25 patents.

About the role

Insilico Medicine is seeking a Python Developer experienced in Deep-Learning for tasks in Chemoinformatics and Computational Chemistry.

The candidate will:

• Design new Descriptors/SMILES/Graphs-based DNN architectures in application to chemical tasks.

• Test existing DNN solutions.

• Research chemical space using DNN approaches.

• Implement various pipelines and algorithms in Python.

Requirements:

• At least 2 years of experience in Python3 and TensorFlow.

• A strong background in ML and DL.

• An ability to absorb a large amount of information from various sources.

Desirable skills:

• Chemical background.

• An ability to learn new software fast.

• Be experienced in Linux, RDKit, Scikit-Learn, Numpy, Bash, JupyterLab, Plotly, Pandas.

• Motivation to learn new things

Terms and conditions:

• Fixed salary + annual bonus

• Voluntary health insurance program and language courses (English and Chinese) after the probation period

• Reimbursement of training programs, participation in conferences and webinars, training certificates

• Comfortable office in Technopark Skolkovo [Moscow, Bolshoy blvr 42, b.1]

• Friendly team and warm environment

• Flexible start of working day (from 8 a.m. to 1 p.m.)