Algorithm Engineer for Large Language Models

Job Responsibilities:

Engage in data processing and model development to optimize training sets for various tasks by collecting real-world and simulator data, including multi-modal data like images, video, audio, and 3D point clouds.

Establish a robust training platform utilizing Kubernetes (K8S).

Drive the development and optimization of specialized policy models and future generalist policies to enable multi-tasking applications.

Job Requirements:

A Master's degree or higher in Computer Science, Artificial Intelligence, Software Engineering, or related field, with over 2 years of experience in areas such as imitation learning/reinforcement learning training, task planning, control, and navigation algorithm research.

Proven experience in developing large models (LLM, VLM, etc.) and multi-modal models; proficiency with deep learning frameworks and capable of conducting large-scale data training and optimization.

Deep understanding of data processing and annotation processes with a track record of handling various data types and formats, especially multi-modal data.

Preference for candidates who have published research in top-tier conferences and journals within the field (such as NeurIPS, ICML, ICLR, CVPR, ECCV, ICCV, ACL, EMNLP, MLSys) or equivalent high-level publications.