该职位来源于猎聘 Responsibilities As a key technology leader within the team, you will:
- Define the overarching data and AI technology blueprint to support humanoid robot perception, decision-making, and control.
- Design and implement scalable, high-availability data architectures for AI model training in robotics, materials, and component development.
- Build and maintain robust data pipelines for the collection, storage, processing, and provisioning of large-scale, complex datasets.
- Ensure data quality, governance, and compliance throughout the entire data lifecycle.
- Provide expert guidance on AI model development and deployment across project teams.
- Serve as a subject matter expert in data-driven methodologies and AI architecture.
- Collaborate with motion control and application specialists to integrate sensory and control data for AI training and deployment.
- Develop and maintain infrastructure for foundation models (e.g., LLMs, VLMs, VLAs) to enable robotic perception, reasoning, and action planning.
- Build scalable pipelines for training and fine-tuning large models using both real-world and simulated data.
- Integrate multimodal models (vision-language-action) into robotic systems to enable adaptive task execution.
- Contribute to the development of simulation-to-real pipelines for humanoid robotics. Qualifications
- Master's degree or higher in Computer Science, Data Engineering, Data Science, or a related field, 10+ years of experience in industry or academia.
- Proven expertise in designing data architecture and developing data pipelines (batch and streaming).
- Proficiency with cloud or on-premises data platforms (e.g., AWS, Azure, Hadoop, Spark).
- Strong programming skills in Python and hands-on experience with machine learning frameworks.
- Practical experience with foundation models (e.g., LLMs, VLMs, VLAs) or large-scale AI systems.
- Solid understanding of data management best practices.
- Excellent communication skills in English. Nice-to-Haves:
- Experience with AI/ML techniques such as Reinforcement Learning and Deep Learning.
- Familiarity with robotics data (e.g., sensor data, simulation, telemetry).
- Experience in motion data processing and control signal modeling.
- Background in training, fine-tuning, or integrating foundation models for robotic applications.
- Knowledge of data governance and security standards.
- Understanding of digital twin workflows and control-policy optimization.