Research Scientist Graduate (Generative AI for Science (ByteDance Seed)) - 2026 Start (PhD)

ByteDance

Responsibilities

Our team at ByteDance Seed has been focusing on building foundation models for science, including biology, physics, and chemistry. We are looking for outstanding researchers to join our team and conduct cutting-edge research in AI for Science.

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

  • Design and develop generative AI models for natural sciences, including protein structure prediction, molecular conformation analysis, and computational protein design.
  • Reproduce and evaluate emerging AI/ML methods using public benchmarks and datasets.
  • Work closely with a multidisciplinary drug discovery team, applying innovative algorithms to tackle complex challenges.
  • Stay informed on the latest advancements in scientific research.

Qualifications

Minimum Qualifications:

  • Pursuing a Ph.D. in fields like computer science, electrical computer engineering, or related areas.
  • Strong research background in AI and machine learning with solid publications in leading conferences (e.g., ICML, NeurIPS, ICLR) and journals, encompassing areas like large language models, diffusion models, geometric deep learning, natural language processing, computational protein design, protein structure and conformation prediction, and related fields.
  • Proficient in Python programming and deep learning platforms, such as Pytorch.
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Confirmed 21 hours ago. Posted 6 days ago.

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