Build, San Francisco
Inference is about 90% of compute spend. Economics are heavily driven by inference optimization. We’re hiring someone to make inference cheaper, faster, and good enough that we can scale the data engine and the product without the GPU bill eating the company.
Build believes in the Bitter Lesson http://www.incompleteideas.net/IncIdeas/BitterLesson.html. By taking a general approach of learning from humans, our addressable market is all physical labor.
We are a fully in-person team in San Francisco (Financial District) and Shenzhen (Nanshan), and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both and work across disciplines as needed.
Build AI is the data hyperscaler for Physical AI. We're vertically integrated across hardware, manufacturing, logistics, collection, and model training to scale the physical labor dataset orders of magnitude faster than anyone in the world.
Build AI is an equal opportunity employer. We review every application. If you do not meet every bullet, still apply. Questions: research@build.ai
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CoupangSeoul, South Korea +1
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Source: the Build Ashby job board. We last checked this job 55 min ago. We first saw it 55 min ago. A job on a job board does not always mean that the team interviews people now. We found this job board in a public list. We did not yet compare it with the careers page of the company.
This base pay is above the median for inference and ML systems jobs in the Bay Area with no level in the job title.
We compare the middle of each posted range. This is base pay as posted. The group does not include this job.