Teams at EMETLAB

Design and maintain scalable services, APIs, distributed systems, and data pipelines for AI training data infrastructure. Own systems through design, implementation, testing, deployment, and monitoring. Lead architectural decisions, improve reliability and scalability, mentor engineers, and collaborate with AI/ML, product, and data operations teams. Contribute to CI/CD, containerization, DevOps, and cloud deployment practices.

Analyze contributor performance, dataset quality, production efficiency, and client delivery metrics. Build dashboards and reports, conduct cohort and trend analysis, monitor quality benchmarks, identify data integrity issues, and translate findings into actionable recommendations. Collaborate with Data Operations, Engineering, Product, and Quality Assurance teams to define metrics and evaluate improvements.

Own the end-to-end UI/UX design process for AI data infrastructure products, including research, prototyping, high-fidelity interfaces, design systems, dashboards, and client and contributor workflows. Collaborate with Product, Engineering, and Data Operations teams to create accessible, polished, technically feasible experiences for complex data-rich platforms.