大型模型数据算法工程师（多模态/感知/仿真方向）

Hong Kong flavor responsibilities:

1. Data processing and optimization: Responsible for the data acquisition, cleaning, annotation, and optimization of the embodied intelligence large model, and building a data-model closed loop.

2. Algorithm development and deployment:

a. Multimodal direction: Use multimodal models to process text, image, and video data, and provide key data processing components for the data management platform.

B. Perception direction: optimize the end-to-end data transmission link (audio and video encoding and low-latency transmission), and deploy multimodal algorithms.

C. Simulation direction: generate high-fidelity simulation data with physical engines (Mujoco/isaacim), design data augmentation and reinforcement learning strategies.

3. Tools and system construction: Develop a data full chain tool to improve data processing efficiency; optimize computing performance at the edge or in a simulation environment.

Qualifications:

1. Basic skills:

a. Proficient in Python, familiar with Linux/Git, solid foundation in data structures and algorithms, good coding conventions and engineering skills.

b. Understand CV/NLP/multimodal models, with model optimization (quantization, pruning) or big data processing (Spark/Flink) experience being even better.

c. Computer Science and Technology, Data Science, Information Engineering, Electronic Information and other related professional graduation. Bachelor's degree or above.

2. Directional requirements (satisfy one of the following):

a. General direction: Familiar with large models or multimodal algorithms, able to independently train models and develop data processing tools. Data can be processed in large-scale using distributed technology.

b. Real machine direction: Familiar with network transmission protocols (UDP/TCP), audio and video coding (H264/H265/AAC, etc.), sensor integration and edge deployment (TensorRT/Jetson).

c. Perception direction: Have physics engine (Mujoco/isaacim) or synthetic data generation (GAN/RL) experience. Understand reinforcement learning, imitation learning or synthetic data generation techniques (such as GAN, Diffusion Models)

a. Able to independently research new technologies, solve practical problems, and have good team spirit.

b. Have python front-end and back-end development skills or hardware debugging experience.

c. Computer/Electronics/Robotics-related bachelor's degree, those with papers, open-source project contributions or large factory experience are preferred.