Alignerr is Labelbox's contributor network for model-alignment and RLHF work. Labelbox is an established data-labeling company, and Alignerr is how it sources the human feedback that frontier and enterprise models need — preference labeling, response evaluation, and reference-answer generation across generalist and expert tiers.
Here's how it works in 2026 and where it fits.
What Alignerr hires for
- RLHF preference labeling — ranking model outputs by quality, the core of alignment work.
- Response evaluation — scoring model answers against rubrics across domains.
- Reference-answer generation — writing the gold-standard responses for training.
- Domain-expert tasks — higher-paid work for contributors with verifiable specialist knowledge.
Pay scales with expertise and task complexity — generalist work at the lower end, expert tasks well above it. Backed by Labelbox's infrastructure, payouts and tooling are more polished than many smaller platforms.
The application process
You create a profile and complete qualification tasks that gate access to paid work. The bar is moderate — more accessible than Mercor's senior-credential filter, more selective than commodity microtask sites. Concrete, verifiable expertise in your profile lifts you into the higher-paid task tiers. See our RLHF jobs explainer for context on the work itself.
Who Alignerr is best for
- Contributors with verifiable domain knowledge who want steady RLHF/alignment work.
- People who value polished tooling and reliable payouts — the Labelbox backing shows.
- A solid mid-tier option between the high-bar and commodity platforms.
Where Alignerr fits vs. the rest
Alignerr is a strong mid-tier RLHF platform — run it alongside micro1 for breadth, and apply to Mercor too if you have senior credentials. The full landscape is in our best-platforms guide.
Bottom line
Alignerr is a reliable RLHF and alignment platform backed by Labelbox, well-suited to contributors with verifiable expertise. Apply to Alignerr and browse current listings on our Alignerr listings page.