Computational Biologist
Nabla Bio
Send a CV and why you're interested in working with us to hiring@nabla.bio.
About
Our mission is to generate new therapeutic opportunities by advancing AI and experimental technologies for drug design. 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.
The role
Fueled by partnerships and increasing demand for internal R&D, we will be looking to you to apply and develop ML-guided antibody design and measurement technologies in tight collaboration and feedback with our wet-lab. This will include:
- Developing robust and simple pipelines to ingest, process, and analyze “large N” and “low N” wet-lab measurements of antibody properties
- Applying ML models or fine-tuning existing ones on such data to build models of antibody function
- Using these models to generate new designs with therapeutic-grade properties that meet engineering metrics agreed upon with our partners
- Analyzing such data to confirm/refute ML research hypotheses
- Working closely with wet-lab and dry-lab collaborators to design experiments that advance molecule engineering goals and research goals and help coordinate/plan their execution.
- Working closely with wet- and dry-lab collaborators to establish simple interfaces to hand off designs, log data, centralize it, and make it easily ingestible for analysis
Qualifications
- PhD, 3+ years of industry experience, or equivalent
- An understanding of data science, statistics, and machine learning fundamentals. Experience applying deep learning models or fine-tuning existing ones.
- Leading of a multi-month computational biology research project that resulted in a publication, or tool that has been impactful for your previous employer, lab, or other users.
- Experience working closely with wet-lab collaborators toward engineering a molecule or a measurement technology. Direct wet-lab experience is a plus.
- Fluency in Python, Pandas, NumPy, and basic ML packages like scikit-learn.
- Experience applying deep learning models implemented PyTorch.
- 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 others