Preference ranking & RLHF
High demandCompare model responses against a rubric, justify the ranking, and handle multi-turn conversations where quality is contested rather than obvious.
Data Gradient is a free platform for annotators to get trained on the latest techniques to improve AI capabilities.
Each skill has a threshold. Clearing it goes on your record. It belongs to you and not to a company. Take it to the next project, the next human data company, the next lab.
The most lucrative opportunities in AI training come from highly complex, frontier campaigns that have rigorous requirements.
A course on translating research and business needs into human-data programs, rubrics, workflows, quality standards, and delivery plans.
Live · Hands-on · CompletedMultimodal courseA multimodal-data course with its most developed module focused on Video Temporal Evidence Grounding: identifying and structuring evidence across time in video.
Live · In progressCourseAn introduction to how AI systems work and where human data supports training, post-training, and evaluation.
LiveCourseA course on writing clear, structured, and testable prompts, defining constraints, and identifying common failure modes.
LiveCourseA course on structuring instructions, examples, tools, and supporting information so AI systems receive the right context.
LiveUpcoming courseAn upcoming course covering pretraining, post-training, evaluation, and the role of human data across the AI lifecycle.
Planned · Join the waitlistWe train you and point you to opportunities. We offer a job board of 1,200+ opportunities.
Start by selecting the course you want to work through. You can focus on one course at a time and return to others whenever you want.
Step 1
Work through the lesson material in the course. This step is about understanding the guidance before you move into the hands-on parts.
Step 2
Complete the activities that are currently available in your course. Activities help you apply what you just learned.
Step 3
After activities, complete the checks that are available to you. Checks are used to confirm completion of the current course steps.
Step 4
When lessons, activities, and checks are complete, you can finish the course. This marks the course as completed in your learning flow.
Step 5
Use Learn to see your course progress over time. You can check what is complete, what is in progress, and what to do next.
Step 6
Every level is earned through graded work on real task types, and each one is recorded on your profile.
You’ve finished the lessons and knowledge checks for a task type. This is a starting point and not a qualification. It doesn’t open any work on its own.
Your scored practice work is reviewed against a client’s rubric. This level does not currently unlock automatic project visibility or matching.
Reviewer access is a planned credential level and is not currently available. It will let annotators review work and help calibrate rubrics once launched.
Course-author access is a planned credential level and is not currently available. It will let contributors create Data Gradient courses once launched.
We provide specific training to help you land the career of your dreams.
Compare model responses against a rubric, justify the ranking, and handle multi-turn conversations where quality is contested rather than obvious.
Write the reference answer a model should have given. Strong writing matters more than technical background. This is the track most people underestimate.
Image, video, and audio labeling with structured schemas: bounding, segmentation, action and affordance tagging, and temporal consistency.
Probe models for failures, document reproducible attacks, and rate severity. This track is planned and not currently available.
Grade agent runs, diffs, and repair traces. This track is planned and not currently available.
Medicine, law, finance, and science. Credential verification required, and market pay reflects the scarcity of qualified reviewers.
Data Gradient’s training made the data annotation process clear and practical. The real examples and guided exercises helped me sharpen my attention to detail and understand how annotation works in real AI workflows. The skills I gained directly helped me complete an assignment and secure a role as an Advanced Data Annotator at a stealth AI company.
A single workshop with Data Gradient completely changed how I approach unstructured data. I learned how to produce clean, semantically structured outputs that AI models can actually use. Those skills directly helped me land a role at an AI automation startup.
Data Gradient’s training transformed my understanding of annotation. What once seemed simple became a structured discipline with clear standards, edge-case reasoning, and QA practices. Shortly after completing the training, I received an interview, and the skills I learned were exactly what interviewers asked about.
The training was thorough, practical, and easy to follow. Working through real examples and annotating together as a group helped me quickly improve my approach. The hands-on experience prepared me well for real assignments, and I’d recommend this training to anyone looking to build strong, practical data skills.
Every platform I’d used before just told me my score dropped. This one told me which part of the rubric I was missing and gave me twenty tasks on exactly that.
Being able to show a client an actual acceptance rate instead of saying “trust me, I’ve done this” is the whole difference. I stopped competing on price.
No. Training, practice, and qualification are free, and they stay free. Clients pay for courses built on their guidelines. You are not the customer, and we don't sell your data.
Data Gradient doesn't pay you. Learn does list more than 1,200 jobs, imported from the public careers pages of human data companies and marketplaces and refreshed regularly. You can learn the skills and find your next opportunity in the same place. Contracts and pay are between you and the client or vendor. We don't take a cut of your rate.
Not for most tracks. Preference ranking and multimodal annotation have no prerequisites, and SFT writing needs only a writing sample. You can start right away. Code review (planned) needs programming experience and expert domain review needs a verifiable credential, because the work genuinely requires them.
Median time to finish a first course is 11 days at a few hours a day. There's no deadline and no penalty for taking longer.
You retake it. Failing isn't recorded as a mark against you.
Yes. The credential is yours. It's portable between projects, agencies, and labs, and anyone you share it with can verify it. If you stop using Data Gradient, you keep it.
A computer with a stable connection and a modern browser. Everything runs in the browser. That includes the practice tasks. Some multimodal and audio tracks recommend headphones and a larger screen.
Training is free, the practice is real work, and the credential is yours to keep.