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The AI platform for human intelligence | micro1 We partner with leading AI labs and enterprises to vet human intelligence, curate world-class teams, and transform their expertise into the highest-quality AI training data
Getting started with micro1 This page contains everything you need to know about getting started with micro1 Here, you can access short videos explaining how to take advantage of all the features our AI recruitment platform has to offer
The human intelligence engine for AGI - micro1. ai The micro1 recruitment engine sources and vets human intelligence at massive scale to rapidly identify true subject-matter experts Our platform forges intelligence into structured, high quality training data that is reliable, reproducible, and reinforced through rigorous evaluations to accelerate AI development
Work with frontier AI labs in Silicon Valley, 100% remote - micro1 Our AI provides personalized company recommendations to help guide your application process How does micro1 ensure fairness in its hiring process? Our AI-powered system assesses and matches candidates based on skills only, eliminating the potential of bias and promoting hiring based on meritocracy
$35M Series A | micro1 The AI interviewer helps us present candidates as far more than a résumé, matching them with jobs they’re best suited for Talent performance management provides ground-truth performance data on the job, to train our predictive models And our data platform is used by leading AI labs and Fortune 10s in a new class of highly valued work: evaluating training AI models
Case studies - micro1. ai With micro1, we reduced our interview staff from 33 to 12 while still conducting 10,000 interviews each month Now, the team is focused on higher-impact work Huge kudos to you guys, first really useful AI applications i've come across
Your complete micro1 AI interview guide Learn how our AI-powered interview works, what to expect, and how to best prepare to succeed in your journey with micro1
AI Engineer interview questions - micro1 An AI Engineer with 6+ years of experience carefully evaluates trade-offs between bias and variance, data preprocessing efforts, and how easily the selected algorithm can be updated or deployed in production What are the most effective strategies for optimizing deep learning model performance and reducing overfitting?