Research Engineer, Data
About Cartesia
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.
About The Role
Data is one of the most critical inputs to frontier AI. In this role, you'll work across the full data stack - from infrastructure and web-scale data pipelines to modeling and evaluation. You'll develop a deep understanding of how data influences model performance and leverage that knowledge to advance the frontier of multimodal datasets to create new capabilities for AI.
- Design and build high-quality datasets for model training and run controlled modeling experiments to measure their impact on model performance and behavior.
- Engineer web-scale data pipelines and build systems to annotate and ensure data quality at scale.
- Develop techniques for post-training and synthetic data generation to improve model quality and intelligence.
What You Bring
- Experience building or working with large multilingual datasets
- Experience with generative models (speech, text, or multimodal)
- Ability to help guide human annotation and evaluation across multiple languages
- Strong applied ML background with a focus on data-centric approaches
- Excitement for building scalable systems that bridge research and production