ByteDance accelerates robotics commercialisation with new recruitment drive

ByteDance, the Beijing-based owner of TikTok and Douyin, is ramping up its efforts in embodied artificial intelligence by accelerating its robotics commercialisation efforts. The company has launched a recruitment drive, hiring for approximately a dozen positions in robotics at its internal unit, Seed, which focuses on AI research and large language model development. Most of these positions are based in Beijing and cover the entire robotics development pipeline, from software and data roles to hardware development.

The company is seeking specialists capable of supporting end-to-end task execution and building data-collection and model-training systems. On the hardware side, ByteDance is looking for engineers capable of full-stack hardware development for humanoids and robotic arms, including a "mass production hardware product manager" who will guide robotics from demos to stable delivery. The monthly salary for this role ranges from 30,000 yuan to 40,000 yuan.

This hiring push comes after the Seed team lost several key AI scientists in recent months. Despite ByteDance's relatively low profile in robotics, the company has quietly developed a presence in the field. Last year, ByteDance unveiled GR-3, a large vision-language-action model designed to help robots perform household chores. ByteDance has also strengthened its hardware push through external investments, joining a 1 billion yuan funding round for domestic AI robotics start-up X-Bot in January, alongside venture capital firm HSG.

China has become a driving force in global shipments of robots, particularly humanoids, with the country accounting for more than four out of five humanoid robot installations in 2025. Alibaba owns the South China Morning Post. While China's transition from robotic demos to full-scale commercialisation is taking longer than expected, analysts remain optimistic about the long-term outlook for embodied AI.

Short-term commercialisation progress is projected to remain modest, with the manufacturing and logistics sectors being the most promising use cases for initial applications.