Human Data Manager, New Grad

An entry-level operations role at micro1 coordinating the human data pipelines that feed AI lab training and evaluation projects. You would recruit and onboard domain experts, write task instructions, audit submitted work for quality, and report throughput and error trends back to the client.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

What the work involves

Human data managers sit between an AI lab's requirements and the expert contributors who produce the data. Day to day that means translating a vague client brief into a task spec a contributor can actually follow, staffing the project from micro1's expert pool, and then spending a large share of your hours reading submitted work — prompts, reference answers, rubric scores, rejection rationales — and deciding what passes. When quality drifts you diagnose whether the cause is the instructions, the rubric, the screening bar, or a handful of specific contributors, and fix the one that's actually broken.

Expect a mix of judgment work and unglamorous coordination: calibration sessions with reviewers, spreadsheet and dashboard hygiene, chasing throughput on a deadline, writing the weekly delivery update, and handling escalations where two qualified experts disagree about a gold label. New-grad hires typically start supporting a single pod or workstream and take ownership of a project within a few months.

What the platform screens for

Reasoning you can show. micro1's screening is AI-led and conversational; it probes follow-ups rather than accepting a first answer. Vague process talk gets unwound quickly.
Written clarity. Instruction-writing is the core deliverable of the job, so expect to be asked to explain something technical plainly or to critique an ambiguous prompt.
Quality judgment under ambiguity. Scenarios about borderline submissions, suspected LLM-generated work, and contributor disputes are common.
Ownership signals. Concrete examples of a project or team you actually ran — course, research lab, internship, student org — matter more than brand names.

Logistics

Remote and largely async, with some overlap expected for client and contributor calls; micro1 works with US-based AI labs, so early-overlap hours are common for candidates outside the Americas. This is a full-time role rather than per-task contributor work, and volume is genuinely project-driven — crunch around delivery milestones is normal. Compensation is not disclosed in the listing; treat any figure you see quoted elsewhere as observed rather than guaranteed and confirm the band directly with the recruiter before investing time.

What they look for

Project Management
Data Operations
Quality Analyst
Human Data

Before you apply

Would you pass this screen?

Drawn from what this listing actually requires. Answered in your browser — nothing is sent to us, and we do not store your answers.

Do you hold a bachelor's degree, or have you completed an equivalent credential (such as an industry bootcamp diploma, professional certification, or demonstrated equivalent experience)?
Undergraduate degree or equivalent · required
Can you provide examples or documentation of a project you managed, ran, or took significant ownership of — such as a course, research study, internship project, or student organization initiative?
Portfolio or project evidence · preferred
Are you available for some early-morning or early-evening overlap with US business hours, or can you arrange work times that align with early-overlap periods common for this role?
US timezone overlap availability · preferred

0 of 3 answered

Included with this listing

Interview prep — 11 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: Can you show the reasoning behind how you clarified ambiguity, not just that you did it? Did you test whether the instructions actually worked?
A strong answer: Name a specific project (course, lab, internship, org) where you inherited unclear requirements, show what was actually ambiguous, describe the questions you asked or steps you took to resolve it, and ideally mention feedback that proved your clarified version worked.
Where people lose it: Telling a story about vagueness being resolved without showing your own role in the diagnosis — saying 'my manager clarified it' or 'we discussed it' doesn't prove you can do this work yourself.