At a Glance
- Tasks: Train AI models by solving complex maths problems and evaluating their outputs.
- Company: Join a cutting-edge team focused on advancing AI technology.
- Benefits: Enjoy remote work, flexible hours, and competitive pay starting at £30.36 per hour.
- Other info: Choose your projects and work at your own pace.
- Why this job: Make an impact in AI while working on projects that excite you.
- Qualifications: Advanced maths knowledge; Masters/PhD preferred but not essential.
The predicted salary is between 63000 - 84000 £ per year.
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Join to apply for the AI Trainer - Mathematics role at DataAnnotation
This range is provided by DataAnnotation. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range
We are looking for a mathematician with an advanced degree to join our team to train AI models. You will measure the progress of these AI chatbots, evaluate their logic, and solve problems to improve the quality of each model.
Make sure to read the full description below, and please apply immediately if you are confident you meet all the requirements.
In this role you will need to hold an expert level of mathematical reasoning- a completed or in progress Masters/PhD is preferred but not required. Other related fields include, but are not limited to: Applied Math and/or Computer Science.
Benefits
- This is a full-time or part-time REMOTE position
- You’ll be able to choose which projects you want to work on
- You can work on your own schedule
- Projects are paid hourly starting at $40+ USD per hour, with bonuses on high-quality and high-volume work
- Give AI chatbots diverse and complex mathematics problems and evaluate their outputs
- Evaluate the quality produced by AI models for correctness and performance
- Fluency in English (native or bilingual level)
- Detail-oriented
- Proficient in arithmetic, algebra, geometry, calculus, probability, statistics, and inductive/ deductive reasoning
- A current, in progress, or completed Masters and/or PhD is preferred but not required
#INDUKMA
Job Types: Full-time, Part-time
Pay: From £29.06 per hour
Expected hours: 1 – 40 per week
Location:
- London (required)
Seniority level
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We think this is how you could land AI Trainer - Mathematics in London
✨Dive into Digital Learning Communities
Join online forums and communities focused on digital education, like EdTech Meetups or LinkedIn groups. Engage actively—share ideas and insights, and you'll not only learn a ton but also connect with potential employers and thought leaders in the industry.
✨Showcase Your Teaching Skills
Create content that demonstrates your understanding of digital learning—think blogs, videos, or even free online workshops. This could catch the eye of companies like DataAnnotation looking for innovative thinkers. Plus, it’s a great way to practice your presentation skills!
✨Utilise University Career Services
If you're still in education or have access to an alumni network, make the most of your university's career services. They often have exclusive job listings and workshops tailored to students wanting to break into full-time roles in education technology.
✨Apply Directly Via Company Websites
When you come across roles like AI Trainer - Mathematics at DataAnnotation, make sure to apply directly through their website. It not only shows you're serious but sometimes you'll find positions listed there that may not be on job boards yet. Keep an eye on those!
We think you need these skills to ace AI Trainer - Mathematics in London
Some tips for your application 🫡
Show Off Your Passion for Learning:When applying for a role in digital learning and education at DataAnnotation, make sure to sprinkle your application with examples of your enthusiasm for teaching and learning technologies. Share specific experiences where you've used, designed, or implemented digital learning solutions, as this shows us that you truly get the field.
Highlight Relevant Education and Certification:In the digital learning space, having a strong educational background can set you apart. Don't shy away from showcasing your degrees, certifications, or relevant courses related to e-learning, instructional design, or educational technology. We love seeing candidates who have actively invested in their learning journey!
Tailor Your CV to the Role:Your CV is your first impression, so let's make it count! Focus on your skills directly related to digital learning, such as course development, learning management systems, and data analysis. Be sure to show measurable results from your past roles to demonstrate your impact and effectiveness.
Craft a Compelling Cover Letter:In your cover letter, express not just your qualifications, but your vision for the future of digital education. Discuss how your experiences align with the mission of DataAnnotation and highlight any innovative ideas you might have for enhancing learning experiences. This is your chance to connect with us on a deeper level!
How to prepare for a job interview at DataAnnotation
✨Get Hands-On with Learning Technologies
Familiarise yourself with common digital learning tools and platforms, like LMS systems or authoring tools that DataAnnotation might use. Understanding these will give you an edge and show you’re ready to jump in from day one!
✨Showcase Your Creativity in Lesson Design
Prepare a portfolio of your past projects if you have them, or create sample lesson plans or digital content demos. Being able to discuss your creative approach to engaging students can really set you apart in a full-time role at DataAnnotation.
✨Brush Up on Educational Trends
Stay in the loop with the latest trends in digital education, like gamification or personalised learning experiences. This will help you engage in meaningful discussions during the interview and demonstrate your passion for the field.
✨Prepare for Collaborative Scenarios
Expect to be asked about your experience working collaboratively with educators or other stakeholders. Consider discussing how you've contributed to a team effort in creating engaging educational content or improving student outcomes.
