You’ve heard us say that quality training data is at the center of AI. The core of that training data is the crowd contributor. More than one million contributors make the work we do possible and help us to produce the highest quality training data for our customers. Our crowd works hand-in-hand with machine learning assistance to efficiently and accurately label data for thousands of different projects in over 235 different languages.
What Our Appen Crowd Contributors Do
We work with our contributors to build training datasets for our customers. A training dataset is used to train AI algorithms and machine learning models. AI must be trained on a dataset in order to return accurate results. The higher the quality and more representative the training dataset, the better the algorithm will run. We work with millions of data points to train create datasets that are labeled and/or annotated by contributors all over the world.
Our crowd contributors are in control of how they work. They select their projects, work on their own schedule from any location they choose, as long as they have an internet connection. This flexibility and our fair pay got us the award for #1 company with remote jobs by FlexJobs for five consecutive years.
There’s important work happening in our crowd, and we’d like to celebrate the that effort by introducing you to them.
Meet: Hediye Cengiz
Hediye Cengiz is a multi-lingual mother of two daughters, lover books and travel with a passion for learning. Living in Turkey, she spends her time reading books, traveling as much as she can, and learning new languages. She also enjoys spoiling her cute cat. Hediye is currently studying Arabic language and literature at Ankara University. She speaks Turkish, Arabic, English, French, Spanish, and Tamazight.
Hediye has worked with Appen for three years with her current focus on content relevance projects.
Why Work for Appen?
Our contributors choose to work with Appen for a variety of reasons. We often hear that it’s the flexibility and fair pay. “Appen not only gives me time to take care of my children, but also gives me the opportunity to work and earn additional income in the remaining time,” says Hediye. “With the money I earn from the projects in Appen, I can spend both on my children and myself. I can even say that I had the opportunity to go on vacations with the remaining money that I earned from different projects.”
We pride ourselves on providing fair pay to our contributors around the world so that they have the compensation they need to take care of their families. We’re always adding more projects to our list of opportunities. “I’ve worked remotely for different companies before, but none offered as regular work as Appen,” says Hediye. “I know that Appen has over a million independent contributors. Having so many people working on dozens of projects and carrying out these works with almost zero problems is a great success in my opinion.”
The final words from Hediye during our interview were some of the most meaningful to us: “I hope that I will have the opportunity to work on different projects under the umbrella of Appen for many years. I would like to sincerely thank Appen for the opportunities it has given me.”
Thank you, Hediye, for being part of our crowd contributor community and ensuring that we work together to create the highest quality data possible and push the world of AI forward to be the best it can be.
You can read about more crowd member on our blog.
How You Can Become and Appen Crowd Contributor
Looking for flexible work hours and fair pay? You can become an Appen contributor, just like Hediye. To get started, visit our jobs page and follow these simple steps.
1. Select a project that interest you and read the instructions.
2. Apple to work on the task
3. Start tasking! We track your task accuracy. A high accuracy rate means you can level up on work on more projects.
We hope you’ll join our Appen Contributor team and find meaningful, flexible work that will support your lifestyle and provide you with the extra income that you’re looking for.