Machine Learning Research Scientist – Atomistic AI - 2026 Start (PhD)

About the Role

The AI for Science team at ByteDance focuses on tackling challenges in natural sciences, including biology, physics, and materials, using computational tools such as Machine Learning, Computational Chemistry, and High-throughput Computation. The team's goal is to create breakthroughs in natural science with new methodologies and contribute positively to the world.

We are seeking talented individuals to join our team in 2026. This role offers unparalleled opportunities to kickstart your career, pursue bold ideas, and explore limitless growth. Successful candidates must be able to commit to an onboarding date by the end of 2026. Candidates can apply to a maximum of two positions globally across ByteDance and its affiliates. Applications are reviewed on a rolling basis, so early application is encouraged.

Responsibilities

• 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.

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

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