A Chinese robotics startup with a Tesla Optimus rival is seeking a new chief scientist with an $18 million salary
The race for AI and robotics talent is heating up and getting expensive.
UBTech is one of the biggest names in China's fast-growing humanoid robot industry.
Song Jiaru/VCG via Getty Images
2026-04-06T10:20:48.765Z
-
The race for AI and robotics talent is getting expensive.
-
Chinese humanoid-robot startup UBTech is hiring a chief AI scientist with a maximum salary of $18 million.
-
This comes as China seeks to build on its robotics dominance.
The AI talent war is spreading to robotics.
Chinese humanoid robotics startup UBTech announced this month that it was seeking a new Chief Scientist with a maximum salary of 124 million yuan ($18 million), per a translated job listing.
The top-end pay sits some way below the most eye-watering earnings offered during the scramble for AI talent, with Meta and OpenAI previously accusing each other of trying to poach star talent with paydays of up to $100 million.
But it marks a departure for China's fast-growing AI and robotics industries, which have until now appeared to avoid the vast payouts that have shaken up Silicon Valley — signaling that China is becoming more aggressive in attracting top-end talent.
UBTech was founded in 2012 and has grown into one of China's most prominent humanoid robot companies.
The Shenzhen-based startup's main product is the 5-foot-9 Walker S2 humanoid robot, which, like Tesla's Optimus robot, is designed to operate autonomously and work in factories. UBTech said earlier this year it struck a deal with Airbus to test its Walker S2 humanoids on factory production lines.
In a job listing posted this month, UBTech said the salary range for the position of "Chief Scientist of Embodied Intelligence" would span from 15 million to 124 million yuan ($2.2 million to $18 million).
The executive will be responsible for accelerating UBTech's humanoid robotics push into manufacturing, services, and "family companionship," per the translated job listing.
Chinese firms appear to be ahead in deploying humanoid robots, with nearly 90% of global shipments last year coming from Chinese companies, according to data from research firm Omdia.
Locally built robots also played a major role in China's Spring Festival, a major public showcase of the country's most cutting-edge tech, with humanoids from Unitree performing kung-fu and acrobatics.
Elon Musk said in a January earnings call that the biggest competition for Tesla's Optimus robot will come from China — although he said he expects Optimus, which begins mass production this year, to outperform any robot under development in China.
Business Insider
https://www.businessinsider.com/chinese-robotics-startup-tesla-rival-18-million-salary-chief-scientist-2026-4Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
startupmillion
‘We got massively outgunned overnight’: Indian startup founder shares how Google, ChatGPT crushed his AI startup - The Financial Express
‘We got massively outgunned overnight’: Indian startup founder shares how Google, ChatGPT crushed his AI startup The Financial Express

Why Your AI Agent Keeps Getting It Wrong: The Three-Layer Architecture Every Data Leader Needs to…
Why Your AI Agent Keeps Getting It Wrong: The Three-Layer Architecture Every Data Leader Needs to Know Your AI agent is not failing because the model is bad. It is failing because the architecture feeding the model is incomplete. The agent does not know what your “revenue” number means. It cannot see the CRM data it needs. It does not know that this question should be answered by the finance persona, not the sales one. The model is doing its job. The infrastructure around it is not. This is the defining challenge of enterprise AI in 2026. Everyone has deployed agents. Most of those agents produce responses that are confidently wrong, inconsistently right, or too generic to act on. The gap between a demo that impresses and an agent that actually drives business outcomes comes down to three

Startup Battlefield 200 applications open: A chance for VC access, TechCrunch coverage, and $100K
Nominate your startup, or one you know that deserves the spotlight, and finish the process by applying. Selected 200 have a chance at VC access, TechCrunch coverage, and $100K for Startup Battlefield 200. Applications close on May 27.
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products

ContractShield: Bridging Semantic-Structural Gaps via Hierarchical Cross-Modal Fusion for Multi-Label Vulnerability Detection in Obfuscated Smart Contracts
arXiv:2604.02771v1 Announce Type: new Abstract: Smart contracts are increasingly targeted by adversaries employing obfuscation techniques such as bogus code injection and control flow manipulation to evade vulnerability detection. Existing multimodal methods often process semantic, temporal, and structural features in isolation and fuse them using simple strategies such as concatenation, which neglects cross-modal interactions and weakens robustness, as obfuscation of a single modality can sharply degrade detection accuracy. To address these challenges, we propose ContractShield, a robust multimodal framework with a novel fusion mechanism that effectively correlates multiple complementary features through a three-level fusion. Self-attention first identifies patterns that indicate vulnerab

Why Your AI Agent Keeps Getting It Wrong: The Three-Layer Architecture Every Data Leader Needs to…
Why Your AI Agent Keeps Getting It Wrong: The Three-Layer Architecture Every Data Leader Needs to Know Your AI agent is not failing because the model is bad. It is failing because the architecture feeding the model is incomplete. The agent does not know what your “revenue” number means. It cannot see the CRM data it needs. It does not know that this question should be answered by the finance persona, not the sales one. The model is doing its job. The infrastructure around it is not. This is the defining challenge of enterprise AI in 2026. Everyone has deployed agents. Most of those agents produce responses that are confidently wrong, inconsistently right, or too generic to act on. The gap between a demo that impresses and an agent that actually drives business outcomes comes down to three



Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!