Lead a high-growth data team responsible for building web-scale data infrastructure, state-of-the-art multimodal datasets, and data-centric modeling to enhance AI model quality at Cartesia. Oversee 6–12 engineers and researchers, setting technical direction for petabyte-scale data processing, annotation models, and empirical data quality frameworks. Partner closely with research and modeling teams to ensure data directly improves AI performance.
Key Highlights
Lead a team of 6–12 data engineers and researchers, scaling from core infrastructure to dedicated modeling efforts
Set technical direction for web-scale multimodal data processing, storage, and serving infrastructure
Drive creation of SOTA multimodal pre-training datasets with rigorous data quality evaluation frameworks
Key Responsibilities
Lead and grow a high-caliber team of data infrastructure engineers and data-focused researchers
Define and implement web-scale systems for processing, storing, and serving multimodal data
Develop state-of-the-art pre-training datasets with advanced annotation models and curation strategies
Establish empirical frameworks to evaluate and improve data quality for AI model performance
Collaborate with modeling and research teams to ensure data directly enhances model capabilities
Hire, mentor, and develop engineers and researchers as the team scales
Technical Skills Required
Data Infrastructure at Scale
Multimodal Data Processing
Large-Scale Data Engineering
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Benefits & Perks
Competitive base salary + equity package
Fully covered medical, dental, and vision insurance
9 weeks paternity leave, 12 weeks maternity leave
401(k) retirement plan
Commuter allowance
Flexible PTO
Daily meals and snacks provided
Nice to Have
Previous modeling experience
Experience building data for generative modeling organizations
Familiarity with multimodal data (audio, speech, and beyond)
Job Description
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.
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