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Paid In-Person Study Near Pasadena - San Marino, CA

Mindrift (Toloka) Mindrift Part-timeLos Angeles, California, United States $200 per task
Posted Oct 28, 2026 · ✓ Still open — checked 1 hour ago

Quick facts

Pay
$200 per task
Type
Part-time
Location
Los Angeles, California, United States
Platform
Mindrift — 674 open jobs
Typical pay at Mindrift
$73–100/hour · median $73 (266 jobs on our site)
Payout
Bank transfer, PayPal, Tipalti, Payoneer (twice a month · min $5)
Apply
Free to apply — never pay to get AI training work

This role is also open in other regions

Mindrift is running an in-person data collection study on how people perform everyday household tasks, like cooking or tackling a DIY project. We're looking for people in the Los Angeles area to join on-site sessions in San Marino or Mid-City Los Angeles.

What you'll do

  • Spend a full session (8 hours) with our on-site team in San Marino or Mid-City LA.
  • Complete everyday household tasks, such as cooking or simple DIY and home-improvement projects.
  • Wear recording devices, including a head-mounted camera, while you work.
  • Follow simple step-by-step guidance from our team.

Who we're looking for

  • 18+ and living within commuting distance of Los Angeles.
  • Available for a full on-site session (mornings, evenings or weekends).
  • Able to stand or walk for up to 8 hours with breaks, lift up to 25 lbs, and bend, kneel and reach overhead.
  • Comfortable using both hands for cooking and DIY tasks.
  • Comfortable communicating and following instructions in English.

Great fit for Anyone with a flexible schedule.

Compensation

$200 per full session (8 hours on-site) or $100 per half of the session - sent to your Mindrift account. You can take part in more than one session.

How to apply

Register on Mindrift and complete a short screening questionnaire. We'll reach out to schedule your session once you're shortlisted.

Mindrift is the AI-tutor platform from Toloka. It recruits writers, editors and domain experts to create and review data used to fine-tune large language models.