Machine Learning Research Scientist – Atomistic AI

About the Role

The AI for Science team at ByteDance is dedicated to addressing challenges in natural sciences, including biology, physics, and materials, through computational tools such as Machine Learning, Computational Chemistry, and High-throughput Computation. The team's mission is to achieve breakthroughs in natural science using novel methodologies and to 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 opportunities, co-creating a future driven by your inspiration with ByteDance.

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.

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

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