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micro1 is a data lab that helps AI labs & Enterprises train foundational models and agents. We provide frontier evaluations and reinforcement learning environments to improve LLM capabilities, as well as contextual evaluations to monitor and improve AI agents in enterprise settings.
Our AI recruiting agent sources and vets domain experts for our data platform that enables rapid production of high-quality training data.
Pay: $85 - $185/hour
micro1 is a data lab that helps AI labs & Enterprises train foundational models and agents. We provide frontier evaluations and reinforcement learning environments to improve LLM capabilities, as well as contextual evaluations to monitor and improve AI agents in enterprise settings.
Our AI recruiting agent sources and vets domain experts for our data platform that enables rapid production of high-quality training data. Our research spans high-value, specialized domains (legal, medical, finance, coding, etc.), where expert-level data is the hardest to produce and most valuable for advanced model performance. Our pipeline performance system ensures both quality and velocity.
Principal Engineer / Professor — Technical Expert
Contractor
Remote
Pay: $80- $185/hour
Commitment: Flexible — fits alongside your current role
Location: Fully remote
You’ll need: Master’s or PhD in an engineering or physical science field, plus principal-level industry experience or a faculty appointment
Build challenging technical problems from your own domain and define what makes an answer correct
micro1 is engaging principal-level engineers, scientists, and faculty to design the technical problems that measure what AI systems can actually do. Your domain expertise is what matters here — no prior AI experience required.
We’re building this across four areas: aerospace, semiconductors, manufacturing and industrial engineering, and materials science. If your work sits in one of those — or across two of them — this is for you.
The work draws on what you already know: the problems where the textbook answer isn’t enough, where the right approach depends on reading the constraints correctly, where a plausible-looking solution is quietly wrong.
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