Product Engineer

Design and build scalable data infrastructure, integrating high-performance databases (relational, NoSQL, cloud-native) with distributed systems for data processing, storage, and streaming. Optimize database systems for performance, reliability, and scalability, ensuring efficient data retrieval, indexing, and querying to support AI workflows. Develop and maintain data pipelines using distributed queues, message brokers, and job management mechanisms to enable high-throughput import/export operations. Collaborate with team members and stakeholders to align data infrastructure with platform goals and customer needs. Participate in Sprint Planning, Standups, and related activities to drive data-focused initiatives forward. Mentor and guide less experienced engineers, sharing expertise in data infrastructure and database optimization. Support the team's area of ownership by working with the Support organization to resolve customer-facing data issues. Stay abreast of industry trends in data infrastructure and database technologies, incorporating relevant innovations into our systems. Contribute to technical documentation, research publications, blog posts, and presentations at conferences and forums. Innovation in AI: Enhance data infrastructure capabilities for an AI platform used by leading AI labs to develop powerful multi-modal large language models (LLMs).

Labelbox offers data labeling solutions for artificial intelligence applications, providing tools to label images, videos, text, and documents efficiently. Their platform allows businesses to create workflows that assign tasks to the appropriate team members, ensuring high-quality results. Unlike competitors, Labelbox also provides workforce augmentation services, enabling clients to scale their labeling capacity with external teams. The company's goal is to enhance AI development by improving the efficiency and quality of data labeling across various industries.