Labelbox
A Material Science Expert at Alignerr supports the development and training of cutting-edge AI models by providing deep domain expertise in materials science. The role focuses on creating and reviewing advanced, real-world-relevant materials science problems and ensuring scientific rigor in all outputs. The position involves collaborating with AI researchers and other domain experts in a remote, asynchronous environment.
Headquarters
San Francisco, California, United States
Company Size
51-200 employees
Founded
2018
Labelbox is a data labeling and data‑ops platform that helps AI teams "build, operate, and staff" scalable data factories for training machine learning models. It combines a web annotation suite (images, video, LiDAR, and text), workflow and quality controls, data management and metadata tools, plus APIs and integrations to fit into ML pipelines. Alongside the software, Labelbox provides managed labeling services and workforce solutions to scale annotation and maintain label quality. Enterprises use it to accelerate model development, improve label consistency and governance, and operationalize high‑volume labeled data.
Labelbox acquires agentic sales automation startup, Upcraft, to rapidly scale the human expertise powering frontier AI
3 weeks ago
AI’s Unsung Hero: Data Labeling and Expert Evals
Jun 27, 2025
AI training companies are raising billions to get humans to teach chatbots. Here are the startups cashing in.
Sep 25, 2025
Headquarters
San Francisco, CA
Company Size
201-500 employees
Founded
2018
Industry
Technology
Glassdoor Rating
4.2 / 5
Sarah Johnson
Chief Executive Officer
Michael Chen
Chief Technology Officer
Emily Williams
VP of Engineering
David Rodriguez
VP of Product
Jessica Thompson
Chief Financial Officer
Andrew Park
VP of Sales
View leadership team, funding history,
and employee contacts for Labelbox.
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