Research Scientist Graduate (Computational Biology (Seed AI-for-Science - 2026 Start (PhD)

Understanding biological structure central to deciphering the mechanisms of life and advancing drug design. We are developing next-generation, structure-centric, multimodal foundation models that power key applications-from complex structure prediction and functional modeling to de novo molecular design.

We are a cross-disciplinary team of experts in machine learning, structural biology, computational chemistry, and bioinformatics, supported by strong engineering infrastructure and access to large-scale compute resources. We aim to develop open, high-precision, generalizable models that drive breakthroughs in biology and drug discovery.

We offer a collaborative, impact-driven environment at the forefront of biology and artificial intelligence, where your work directly contributes to transformative advances in drug discovery and life sciences. Our team benefits from access to cutting-edge compute infrastructure, proprietary tools, and a culture that values open science. By joining us, you'll have the opportunity to build foundational technologies that shape the future of molecular design and biological understanding.

We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with ByteDance.

Successful candidates must be able to commit to an onboarding date by end of year 2026.

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to ByteDance and its affiliates' jobs globally. Applications will be reviewed on a rolling basis. We encourage you to apply as early as possible.

Responsibilities:1) Collaborate closely with a multidisciplinary team of ML researchers, computational biologists, and chemists to tackle cutting-edge scientific challenges in molecular modeling.2) Contribute to the design, training, and optimization of large-scale models for structure prediction across diverse biomolecular systems, addressing key challenges such as conformational sampling, binding affinity estimation, and de novo molecular generation.3) Translate insights from structural biology, experimental data, and physical principles into scalable model architectures and generative algorithms.