Student Researcher (Foundation Models - Reasoning, Planning & Agent

This position is responsible for researching and building the company's LLMs.

The role involves exploring new applications and solutions for related technologies in areas such as search, recommendation, advertising, content creation, and customer service.

The goal is to meet the increasing demand for intelligent interactions from users and to significantly enhance their lifestyle and communication in the future.

We are looking for talented individuals to join us for a Student Researcher opportunity in 2025.

Student Researcher opportunities at ByteDance aim to offer students industry exposure and hands-on experience.

Turn your ambitions into reality as your inspiration brings infinite opportunities at ByteDance.

The Student Researcher position provides unique opportunities that go beyond the constraints of our standard internship program, allowing for flexibility in duration, time commitment, and location of work.

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 ByteDance and its affiliates' jobs globally.

Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities: * 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.

* 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). * Solve complex tasks via system 2 thinking, leverage advanced decoding strategies such as MCTS, A*. * 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.

* Teach foundation models to use tools, interact with APIs and code interpreters.

Build agents and multi-agents to solve complex tasks.