Machine Learning Research Scientist – Atomistic AI PhD

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

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 to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co‑create a future driven by your inspiration with Byte Dance. 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 Byte Dance 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.

• 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.

• 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

The base salary range for this position in the selected city is $129,960 - $246,240 annually. Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognise employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day‑one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short‑term and long‑term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programmes at any time, with or without notice.

For Los Angeles County (unincorporated) candidates, qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

• Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
• Appropriat…