Audio Data Collector (English Speaker)

Telus International
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Machine Learning Engineer

Audio Machine Learning Engineer

Audio Machine Learning Engineer

Data Scientist, Machine Learning, AI & Data, Defence & Security (DV clearance required)

Research Fellow (Computer Vision)

TELUS International AI-Data Solutions partners with a diverse and vibrant community to help our customers enhance their AI and machine learning models. The work of our AI Community contributes to improving technology and the digital experiences of many people around the world. Our AI Community works in our proprietary AI training platform handling all data types (text, images, audio, video and geo) across 500+ languages and dialects. We offer flexible work-from-home opportunities for people with passion for languages. The jobs are part-time, and there is no fixed schedule.

The Role

Role Description:

  • Participants will have to record utterances displayed on a tool in their own voice using their Android mobile phone or a handheld Android tablet device.
  • Each utterance to be recorded will have specific instructions on how it needs to be recorded and you are expected to follow the instruction strictly.
  • Please note each participant can only complete the project once.
  • Workload:Each participant is expected to record around 185 utterances.
  • The recordings will go through a QA process and only after they are QA passed, will the task be considered as completed and accepted.
  • Utterances falling short of this requirement will be considered as incomplete and will not be compensated.
  • Estimated time to complete the task:1 to 1.5 hours.
  • Expected project duration:April 2024 to May 2024.

Ideal Profile

Requirements:

  • An Android mobile phone or handheld Android tablet device.
  • Native speaker of English language to be able to effectively liaise with stakeholders in the region
  • Located in United Kingdom
  • Fluency in English - To have the ability to understand instructions and communication emails which will be in English.
  • Stable Internet connection for the duration of the task.
  • A quiet environment to record the utterances.

What's on Offer?

  • Earn additional income with flexible hours to fit your lifestyle

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.