Project Lead
2 days ago
Position Summary
As a Project Lead Annotation, you will be responsible for leading large, distributed teamsboth internal and vendor-basedin delivering high-quality annotated datasets for AI and machine learning projects. You will oversee end-to-end project execution, define annotation workflows, configure QA pipelines, and ensure consistent throughput and data accuracy.
This role requires a strong blend of team leadership, process orientation, and hands-on technical know-how of annotation platforms and data labeling tools. The ideal candidate is a structured thinker, effective communicator, and passionate about enabling AI development through precise human-in-the-loop data handling.
Key Responsibilities
- Lead and manage large annotation teams, including third-party vendors and remote contributors
- Assign tasks, define milestones, track progress, and manage workloads to ensure on-time delivery
- Motivate and train team members, fostering a culture of ownership and continuous improvement
- Draft and maintain clear annotation guidelines and ensure adherence across the team
- Design and implement QA strategies to monitor and enhance data quality and consistency
- Optimize workflows, review loops, and escalation paths to improve labeling efficiency
- Collaborate with cross-functional teams including product managers, engineers, and QA leads
- Propose and implement process improvements to enhance throughput and accuracy
- Monitor metrics, analyze team performance, and recommend corrective actions
- Utilize collaboration tools to manage distributed workflows effectively
Qualifications & Experience
- Bachelors or masters degree in computer science, Information Technology, Data Engineering, or a related field
- 3-6 years of experience leading data or annotation projects, with proven experience managing distributed teams
- Hands-on experience with annotation tools such as Label box, CVAT, or other custom labeling platforms
- Strong understanding of data formats such as JSON, XML, YOLO, CSV, and COCO
- Experience in configuring QA workflows, writing annotation guidelines, and driving performance-based output
- Knowledge of AI and machine learning concepts, including RLHF (Reinforcement Learning from Human Feedback)
- Excellent communication and interpersonal skills to work with global teams and clients
- Strong problem-solving, organizational, and multitasking skills
What We Offer
- Competitive compensation package aligned with your experience and contribution
- Flexible benefits to support your professional and personal goals
- A collaborative, experimentation-driven work culture
- Co-funded learning and upskilling programs to accelerate your career.
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