Data Quality Operations Analyst (Contract)

Our mission is to architect AI that learns from and interacts with the world like humans do.

We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models or SSMs, a new primitive for training efficient, large-scale foundation models. Our team combines deep expertise in model innovation and systems engineering paired with a design-minded product engineering team to build and ship cutting edge models and experiences.

We're funded by leading investors at Index Ventures and Lightspeed Venture Partners, along with Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks and others. We're fortunate to have the support of many amazing advisors, and 90+ angels across many industries, including the world's foremost experts in AI.

We are a research lab looking for a Data Quality Operations Analyst to ensure our internal and vendor datasets meet the highest possible standards. As we scale our audio and text data pipelines, you will play a critical role in reducing the auditing load on our core team while maintaining rigorous quality control across multiple languages and workflows.

• Audit large-scale data deliveries from vendors and internal workflows.

• Create, refine, and maintain comprehensive quality guidelines for each specific dataset.

• Provide structured, actionable feedback to both external vendors and internal teams.

• Review and approve recording data voice actors across locales.

• Exceptional attention to detail and a highly organized approach to reviewing large volumes of data.

• Strong communication skills, with the ability to synthesize complex data audits into clear, structured feedback for vendors.

• Operational discipline—you can handle repetitive, high-focus tasks while maintaining a high bar for quality.

• Ability to work independently and manage your own time effectively to meet tight project deadlines.

• Prior experience working with audio data, transcription, or linguistics.

• Proficiency in multiple languages to assist with diverse locale audits.

• Familiarity with data annotation platforms and vendor management