OpenAI is offering robotics engineers base salaries of up to $500,000 a year as the artificial intelligence company accelerates efforts to build robots capable of operating in the physical world.
The company now has 27 robotics-related positions listed on its careers page, up from 11 in May, according to a review by Business Insider. The roles span hardware engineering, software, machine learning, data collection, prototyping and testing, providing a clearer picture of an effort that goes beyond developing AI models to building the physical systems that will use them.
Publicly advertised base salaries for the positions range from $177,000 to $500,000 a year, excluding equity. The highest-paid opening is for a machine-learning engineer focused on distributed data systems, responsible for the infrastructure needed to process and move large quantities of robotics training data across computers.
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The hiring spree comes as OpenAI increases its focus on what the industry calls physical AI, the use of sophisticated AI models to control machines and interact with the physical world.
OpenAI is hiring actuator design engineers to develop the motors and mechanisms that move robotic joints, as well as software, firmware and machine-learning engineers. It is also seeking a laboratory technician to help “develop, build, test, and iterate on robotic systems” and a lawyer dedicated to the robotics team.
Another position would oversee OpenAI’s “data collection facilities,” highlighting one of the central challenges in developing capable robots.
Unlike large language models, which can be trained on enormous quantities of text and other information already available online, robots require data generated through interactions with the physical world. That can include demonstrations of humans performing tasks, robots manipulating objects, and machines responding to changing environments.
Companies developing robots therefore have to generate much of that data themselves or obtain it from external providers. OpenAI’s decision to recruit personnel specifically around data collection suggests that it sees the data pipeline as an important part of its robotics infrastructure.
Guy Hoffman, a Cornell mechanical and aerospace engineering professor who leads the university’s Human-Robot Collaboration and Companionship Lab, reviewed the 27 job postings for Business Insider and said OpenAI appears to be assembling a “custom robot design team.”
The postings also offer an indication of the scale of OpenAI’s ambitions.
One listing says the robotics team is focused on “unlocking general-purpose robotics and pushing towards AGI-level intelligence” while exploring “a broad range of robotics form factors.” Another describes a longer-term vision in which “everyone” could have a personal robot capable of performing whatever tasks they need.
That ambition represents a significant expansion from OpenAI’s earlier robotics work.
OpenAI Moves Beyond Software
CEO Sam Altman has become more direct about the company’s plans to build physical machines.
“We will definitely do a humanoid,” Altman told investor Alex Heath on the Sources podcast earlier this month. “We will do other form factors as well.”
The robotics team is led by Aditya Ramesh, an OpenAI researcher known for creating DALL-E and later working on Sora, the company’s video-generation system. Ramesh has also worked on models designed to simulate aspects of how the physical world operates.
OpenAI previously experimented with robotics before shutting down its robotics project in 2020. That effort became known for a robotic hand capable of solving a Rubik’s Cube. The company’s return to robotics comes as substantial capital flows into physical AI, with technology companies and startups seeking to combine advanced AI models with machines that can perceive, reason and act in physical environments.
OpenAI has already been reported to be training a robotic arm to perform household tasks as part of its humanoid efforts. If the company ultimately develops its own humanoid robots, it will enter a market that includes well-funded efforts from Tesla and Figure AI. Tesla is developing its Optimus humanoid robot, while Figure has previously worked with OpenAI on AI models for robots. OpenAI Startup Fund, a venture fund affiliated with OpenAI, is also an investor in Figure.
The job listings provide few definitive clues about the eventual design of OpenAI’s machines. References to laser range finders and batteries suggest to Hoffman that the company may be considering an untethered mobile robot, although the postings do not establish whether such a machine would travel on wheels or legs.
The hiring pattern also provides clues about areas where OpenAI may be placing less emphasis.
“There is not a lot of electronics work sought, so I don’t think there will be a focus on sensors beyond off-the-shelf cameras, microphones, and laser range finders,” Hoffman said.
Laser range finders use light to measure distance and can help robots map environments and navigate around obstacles.
OpenAI’s strategy appears to be centered on combining its AI expertise with purpose-built robotic systems and the data infrastructure required to train them. That could give the company greater control over the interaction between its models, robotic hardware and training data, rather than relying entirely on third-party manufacturers.
The approach also opens a new cost center. Training physical AI requires not only computing infrastructure but physical facilities, hardware, human demonstrations, testing environments, and large-scale data collection. Altman has pointed to data centers as one potential early use for OpenAI’s robots. Machines operating in such environments could eventually perform physical tasks alongside the company’s broader AI infrastructure.
“There will of course be data center robots that have, like, different form factors,” Altman said. “Someday, I think everyone should have a personal robot.”
The hiring surge indicates that the vision is moving from a largely conceptual ambition toward a broader engineering operation. OpenAI is recruiting across the physical hardware, software, data and testing layers needed to turn AI models into machines that can operate outside a computer screen.