Machine Learning Scientist Graduate (Scaling AI for Biology (AI-for-Science)) - 2025 Start (PhD)

- Entry Level

Seattle, WA

Publishing Industries

About the position

Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Helo, and Resso, as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content. 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. One of our goals is to revolutionize biological science through the development of large-scale foundational models that can be applied to a wide range of biological systems and tasks. To achieve this, we seek to discover model architectures that are both general and scalable in terms of data and parameter size. We are looking for talented individuals to join our team in 2025. 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 2025. We will prioritize candidates who are able to commit to these start dates. Please state your availability and graduation date clearly in your resume. Applications will be reviewed on a rolling basis. We encourage you to apply early.

Responsibilities

• Collaborate closely with team members from diverse fields to deeply understand the data and the context of biological challenges, ensuring that test metrics are aligned with real-world applications.
• Utilize innovative and critical thinking, actively implement your ideas in biological tasks such as complex structure generation, molecular design, and develop unified multimodal models that integrate sequence, graph, structure, function, and properties.
• Stay updated on recent advancements and actively discuss and share insights with team members.