<p>About The Team: Founded in 2023, ByteDance Doubao Team is dedicated to crafting the industry's most advanced LLMs. We aim to lead global research and foster both technological and social progress.</p> <p>With a long-term vision and a strong commitment to the AI field, the Team conducts research in a range of areas including natural language processing (NLP), computer vision (CV), and speech recognition and generation. It boasts a robust international presence with labs and research facilities in China, Singapore, and the US. Leveraging substantial data and computing resources and through continued investment in these domains, our team has built a proprietary general-purpose model with multimodal capabilities. This model supports over 50 downstream business services including Doubao, Coze, and Dreamina, which are available to enterprise customers via the Volcano Engine. Currently, the Doubao App is the most used AIGC application in the China market.</p> <p>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.</p> <p>Successful candidates must be able to commit to an onboarding date by end of year 2025.</p> <p>We will prioritize candidates who are able to commit to these start dates. Please state your availability and graduation date clearly in your resume.</p> <p>Applications will be reviewed on a rolling basis. We encourage you to apply early.</p> <p>Candidates can apply for a maximum of TWO positions and will be considered for jobs in the order you applied for. The application limit is applicable to ByteDance and its affiliates' jobs globally.</p> <p>Responsibilites:</p> <ol> <li>Reasoning and planning for foundation models. Enhance reasoning and planning throughout the entire development process, encompassing data acquisition, model evaluation,pretraining, SFT, reward modeling, and reinforcement learning, to bolster overall performance. </li><li>Synthesize large-scale, high-quality (multi-modal) data through methods such as rewriting, augmentation, and generation to improve the abilities of foundation models in various stages (pretraining, SFT, RLHF). </li><li>Solve complex tasks via system 2 thinking, leverage advanced decoding strategies such as MCTS, A*. </li><li>Investigate and implement robust evaluation methodologies to assess model performance at various stages, unravel the underlying mechanisms and sources of their abilities, and utilize this understanding to drive model improvements. </li><li>Teach foundation models to use tools, interact with APIs and code interpreters. Build agents and multi-agents to solve complex tasks. </li></ol>