Research Scientist

Description:

At ByteDance, our people are humble, intelligent, compassionate and creative. We create to inspire - for you, for us, and for millions of users across all of our products. We lead with curiosity and aim for the highest, never shying away from taking calculated risks and embracing ambiguity as it comes. Here, opportunities are limitless for those who dare to pursue bold ideas that exist just beyond the boundary of possibility. Join us and make an impact happen with a career at ByteDance.

About the team:
The Applied Machine Learning (AML) team is committed to the research and deployment of the next-generation of AI core technologies, such as large pretrained models and device-cloud collaboration learning, as well as their wide applications in practice, including but not limited to search, recommendation, advertising, auditing, federated learning, etc. The team has a strong foundation in scientific research, engineering and product implementation. Team members have rich backgrounds covering NLP, CV, multimodality, graph computing, search and recommendation, federated learning and other fields, and have published more than 100 top-tier conference papers.

Responsibilities:

• Design and deploy state-of-the-art models in various downstream applications, especially code generation and autogpt applications;
• Collaborate with software engineers to build enterprise-level platforms with cutting-edge models;
• Collaborate with open-source communities or academia to advance AI development.
• Candidates with research or technical backgrounds in NLP, code generation, large pretrained models, or participating in large-scale online machine learning platforms are preferred
• Candidates with pre-training foundation technologies, including efficient training and pretraining as a service, downstream applications of NLP/CV/Video are preferred
• Candidates with top-tier conference papers, including NeurIPS, ICML, ICLR, CVPR, ICCV, ACL, KDD, etc., relevant internship experience or winners of ACM competitions are preferred
• Proficient in deep learning frameworks such as PyTorch and TensorFlow, and programming languages such as python or java.