ByteDance

Responsibilities

The AI for Science team has been focusing on tackling challenges in natural sciences, including biology, physics, and materials, with computational tools such as Machine Learning, Computational Chemistry, High-throughput Computation. Our goal is to create breakthroughs in natural science with new methodology and help the world.

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.

• Stay up to date with cutting-edge research and collaborate with the team to develop a broad and in-depth understanding of key technical domains.
• Apply interdisciplinary approaches—combining machine learning, quantum chemistry, molecular dynamics, and other methods—to explore novel applications in biology and materials science.
• Integrate internal and external research outcomes to drive real-world implementation of research achievements and create widespread impact.

Qualifications

Minimum Qualifications:

• Ph.D. degree in Machine Learning, Computational Materials Science, Computational Biology, or a related field is preferred.
• Strong understanding of machine learning algorithms, with extensive hands-on experience and software development knowledge.
• Proven track record of publications in high-impact, peer-reviewed scientific journals.
• Demonstrated ability to conduct independent and innovative research; capable of thriving in a fast-paced, interdisciplinary environment.
• Excellent teamwork and interpersonal communication skills, with the ability to convey complex ideas effectively.

Preferred Qualifications:

• AI force field models and atomic/molecular foundational models
• Molecular dynamics enhanced sampling algorithms integrated with generative models
• Design of biomolecules or material molecules
• Deep engineering optimization and acceleration of AI-driven molecular dynamics simulations