Our mission is to enable pharmaceutical and biotech companies to bring more antibody therapies to patients. Using AI and massively parallel experimentation, we design antibodies that precisely bind the disease target at the right location, while minimizing manufacturability and toxicity risks. We are a well-funded, revenue-generating, bilingual company of wet- and dry-lab scientists, and are founded by AI and protein design experts from Harvard University.
Fueled by partnerships and increasing demand for internal R&D, we will be looking to you to apply and develop ML-guided antibody design technologies in tight collaboration and feedback with our wet-lab. This will include:
- Developing novel strategies and optimizing existing ones to predict antibody function from sequence and structure
- Developing methods to predict and design antibody-antigen interactions
- Develop sequence- and structure-informed representations that enable multi-property antibody engineering
- In collaboration with our wet-lab, designing antibody structures and sequences for functional measurement in frequent design-build-test cycles
- Bachelor’s or master’s degree, with a PhD or equivalent preferred.
- Leading of a multi-month machine learning research project that resulted in a publication, or tool that has been impactful for your previous employer, lab, or other users.
- Strong understanding of statistics and machine learning fundamentals. Practical experience developing deep learning models from scratch, and tuning existing ones.
- Fluency in Python and PyTorch and commonly used higher-level frameworks for model training and hyperparameter tuning.
- Fluency with Unix environments, AWS, and GitHub
- You are problem-focused, and interested in working in a high-intensity, fast-paced environment often driven by deadlines
- You value unblocking colleagues before yourself, and are excited to mentor/train junior colleagues