Data Science Intern, Machine Learning

New Equilibrium Biosciences discovers drugs that target intrinsically disordered proteins (IDPs), with the mission of creating transformative medicines for patients with life-threatening diseases. IDPs are a class of shape-shifting proteins that regulate many cellular systems and include over one third of human proteins. These proteins are highly involved in disease, but their extreme flexibility challenges traditional methods for resolving protein structure, prohibiting rational drug design. New Equilibrium is overcoming this challenge through our computational-experimental IDP-Shift platform, which combines accurate modeling of IDP conformations with experimental biophysics to enable ensemble-based drug design for IDPs. A key component of our platform is our machine learned force field which describes protein dynamics at the quantum chemical level. 

We are recruiting machine learning scientists interns to join our team of highly motivated scientists to expand and improve our machine-learned force field for IDPs. The successful candidate will become a key contributor to our machine learning and data science efforts, focused on developing machine learning models to accurately reproduce quantum chemistry on biomolecular systems. By contributing to our machine learning efforts, they will play an integral role in enabling structure-based drug design for IDPs and discovering new drugs for patients.

• Developing, implementing, and testing machine learning models and tools using PyTorch 
• Training machine learning models on datasets and fine-tuning them to improve accuracy and performance
• Documenting the process and results of machine learning experiments, including code, data, and results
• Communicating the findings of machine learning experiments to team members and stakeholders in a clear and concise manner
• Identifying and addressing challenges that arise during the machine learning process, such as data quality issues or performance limitations
• Designing experiments to test hypotheses and validate machine learning models
• Ensuring the quality of code, data, and analysis

• Degree in computer science or related field with experience in machine learning (Ph.D. preferred)
• Fluent in Python and key machine learning libraries (e.g. PyTorch, TensorFlow, or JAAX)
• Ability to prioritize well and communicate clearly
• Attention to detail and high level of organization

• Experience in applying machine learning to molecular systems
• Proficient with common development tools (e.g. Git and Kanban) and processes (e.g. testing and code reviews)
• Experience in the industry
• Experience with CUDA