Contract Position: Quality Lead

Experience: 3.0 - 20 years

Max Experience: 20

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,”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 Description

Job Title: Quality Lead

Location: Pan India

Experience: 3+ yrs.

Employment Type: Contract to hire

Work Mode: Remote

Notice Period: Immediate joiners

Roles & Responsibilities

Role Overview

Lead QA reviewers to ensure annotated data is accurate, consistent, and delivered on time. The role involves managing review queues, identifying errors, providing feedback, and supporting annotators in a fast-paced environment with tight turnaround times.

Key Responsibilities

• Lead QA reviewers and manage audit and review queues.
• Spot-check annotated data and reviewer decisions.
• Provide clear, actionable, and consistent feedback.
• Conduct office hours and clarify guidelines for annotators.
• Track quality scores, error trends, and reviewer performance.
• Train new reviewers on guidelines, tools, and review processes.
• Escalate recurring issues and unclear guidelines to the Senior QA Lead.

Required Qualifications

• 3–5 years of overall experience, including 1+ year leading or mentoring reviewers.
• Strong attention to detail and ability to follow complex guidelines.
• Clear communication and feedback skills.
• Experience in QA, annotation, data labeling, or AI training data is preferred.