A biotech company is leveraging the adaptive immune system to develop a new generation of precision immunotherapies. Through focused active learning cycles that integrate cutting-edge science with AI-based modeling, the company seeks to unlock the potential of T cell therapeutics and transform personalized healthcare.
This company is assembling a collaborative, ambitious, and innovative team to tackle challenging and impactful biological problems. It aims to translate state-of-the-art research into clinical products that significantly improve patient outcomes. Team members are encouraged to explore profound scientific questions while enjoying flexibility, opportunities for growth, and meaningful responsibilities. The team comprises individuals from diverse backgrounds, united by a shared goal of decoding biology through data science.
The Role
A machine learning research scientist in this team will focus on developing novel AI models to address significant scientific questions in immunology and human biology. This work involves representation learning at multiple scales—from individual molecules to complex immune system repertoires. The goal is to support predictive tasks such as disease classification and generative modeling of protein sequences.
Collaboration with molecular biologists and immunologists to generate data for training and validating models is a key aspect of the role. Working within a multidisciplinary team of researchers and engineers with expertise spanning mathematics, physics, chemistry, engineering, and biology, the scientist will contribute to high-impact projects.
Key Responsibilities
Develop predictive and generative models to learn meaningful representations of the adaptive immune system.
Apply research to diverse applications, including antigen-T cell receptor interaction prediction, T cell receptor sequence engineering, and disease classification.
Collaborate with biologists and engineers to optimize experimental cycles and data generation for model improvement and validation.
Share findings through internal discussions, peer-reviewed journals, conferences, and external collaborations.
Essential Qualifications
A PhD in a mathematical or computational discipline, or equivalent practical experience.
Desirable Qualifications
A proven track record in solving complex problems.
Experience building modern machine learning systems using accelerators.
Knowledge of chemistry, physics, or biology.
Familiarity with biological sequence data, protein structural modeling, or molecular dynamics.
A passion for AI and biology, coupled with a commitment to creating products that benefit humanity.
Enthusiasm for contributing to a fast-paced startup environment and growing alongside the company.