Finding a job
DAT is a work just like any other contract work. Listing it on your resume can really help you find your next role, even if it's the only experience you have so far. I followed these tips from r/dataannotationtech and r/dataannotation—links coming soon—and have seen positive results. These tips are general enough that they should apply to soft skill-based professions just as well as technical ones.
An important note: finding a new role in this market can have its biggest cost in your mental wellbeing. It can take hundreds of applications and constant improvement to land something. If you are struggling, please reach out to someone—a friend, family, even my inbox is open—or call 988.
The information on this page is subject to change as more people share their experiences and the DAT platform changes.
What you should and shouldn't put on your resume
Experience heading
DAT workers are contract workers. Colloquially, this is called gig work and means that DAT's company name will not show up when an employer runs a background check on you. You can still list DAT as the company for the experience, as the name can give more weight than just being self-employed. But it should be clear that the work is contract work—add a parenthetical (Contract) or some other informative technique.
As for the role title itself, you can't really go too wrong. The only thing to keep in mind is that you don't want to give the impression that you were designing LLM architecture. "Researcher" or "ML Engineer" can easily give the impression that the work is designing, shaping, and making decisions on the model implementations themselves. The training we do is separate from the architecture of the models and is specifically in evaluation.
Bullet points
To preface, the Code of Conduct should be respected. You should verify that your resume is inline with the most up-to-date version of the Code of Conduct that you have agreed to before sending any applications.
As for the content itself, the general strategies of training models are publically availible and documented in research papers about alignment: reinforcement learning with human feedback, reinforcement learning with verifiable rewards, LLM-as-a-Judge, and rubrics as rewards. These papers all layout the mainstays of projects that you may be working on. What will differ from person to person is the actual domain-specific prompting, verification, or any other miscellaneous work you did. Everything here can be generally reflected in your resume.
A differentiating niche that can help your resume "break through" is emphasising the intersection of your work and model evaluation. Companies that are looking to integrate LLM/agent workflows in their business need people with hands-on experience with frontier models and evaluting systems that use them. As agents/LLMs are increasingly used in business, this puts you at an advantage.
Quantifying impact
This is the hardest part of selling yourself when preparing job applications. Total number of tasks completed, hours worked on specific domains, and any amount/details of positive admin feedback are a few examples of raw numbers that you can list in your experience. As workers do not see the end results of how their data impacts model releases, this is where you have to pull on anything and be creative.
Example
Everything above may not seem too grounded, so an example of an experience block for DAT is given below:
Software Engineer, AI Training Data (Contract), DataAnnotation.tech – Remote
Oct 2025 – Present
- Rated and ranked 2,000+ model-generated code completions, PR diffs, and multi-step agent traces (Python, Java, C#) for correctness, efficiency, and adherence to task instructions, producing preference labels used to train reward models.
- Reviewed end-to-end coding-agent trajectories (tool calls, terminal output, file edits, test runs) to judge whether the agent’s plan and intermediate steps were sound, not just the final diff, flagging silent failures and premature ‘done’ claims.
- Wrote detailed rationale notes on ~40% of ratings explaining specific bugs, edge-case failures, and reasoning errors, which were used to refine grading rubrics and catch systematic model weaknesses (e.g., mishandled concurrency, incorrect Big-O claims).
Resources
Coming soon