Data Annotator

About Company:-

Our client is a Palo Alto–based AI infrastructure and talent platform founded in 2018. It helps companies connect with remote software developers using AI-powered vetting and matching technology. Originally branded as the “Intelligent Talent Cloud,” Turing enabled companies to “spin up their engineering dream team in the cloud” by sourcing and managing vetted global talent.

In recent years, they have evolved to support AI infrastructure and AGI workflows, offering services in model training, fine-tuning, and deployment—powered by their internal AI platform, ALAN, and backed by a vast talent network. They reported $300 million in revenue and reached profitability. Their growth is driven by demand for annotated training data from AI labs, including major clients like OpenAI, Google, Anthropic, and Meta.

Job Title: Data Annotator

Location: Pan India

Experience: 5+ yrs.

Job Type : Contract to hire.

Notice Period:- Immediate joiners.

What does day-to-day look like:

• Watch short robotic task videos in full before annotating.
• Identify the main objective of the robot’s task as well as the smaller steps it takes.
• Summarize what happened in the video, noting whether the task succeeded, partially succeeded, or failed, and highlight any errors or inefficiencies.
• Provide timestamped annotations that describe, in detail, each robot movement in the video.
• Ensure annotations adhere strictly to structured guidelines to maintain consistency across datasets.
• Review and refine annotations based on feedback to improve accuracy and clarity.

Requirements:

• Strong attention to detail and ability to follow structured guidelines.
• Clear written communication skills in English.
• Comfort with repetitive, precision-focused work.
• Ability to differentiate between successful, partial, and failed task execution.
• Prior experience in data annotation, transcription, or quality assurance is a plus but not required.
• 2 years of experience or more with technical background in any programming language

What success looks like:

• Delivering consistent, accurate annotations across tasks.
• Capturing both the “what” (intent and actions) and the “how” (quality of execution) of robot tasks.
• Contributing to a gold-standard dataset that supports robotics and AI training.