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Cartesia

Develops foundation models with subquadratic architectures

Engineering Manager - Data

Engineering LeadershipUpdated on Nov 11
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • Proven management experience, including directly leading a data team of at least 6 people.
  • Hands-on familiarity with data infrastructure at scale, including processing, storage, and systems that make large-scale data usable.
  • A track record of building and leading data efforts, ideally in the context of a modeling project.

Responsibilities

  • Lead and grow a high-caliber team of data infrastructure engineers and data-focused researchers, splitting focus between shared infrastructure and dedicated modeling teams.
  • Set the technical direction for how Cartesia processes, stores, and serves multimodal data at web scale.
  • Drive the creation of state-of-the-art multimodal pre-training datasets, including the annotation models and curation strategies that determine what the models learn.
  • Establish a rigorous, empirical approach to data quality by training and evaluating models to understand what makes data valuable and applying those findings to dataset decisions.
  • Partner closely with modeling and research teams to ensure data work translates directly into better models.
  • Hire, mentor, and develop engineers and researchers as the team scales from a handful of people to a full organization.
  • Initially manage a team of 6–8 spanning core data infrastructure and individual modeling efforts, and grow it toward approximately 12 as the organization scales.

Desired Qualifications

  • Previous modeling experience.
  • Experience building data for a generative modeling organization.
  • Familiarity with multimodal data, including audio and speech.

About the Company

Cartesia

Develops foundation models with subquadratic architectures

Cartesia.ai develops advanced AI foundation models using new subquadratic architectures and state space models. Its models are designed as underlying systems that can be adapted for many applications and are released in part under open-source licenses (e.g., Apache 2.0), with licensing, partnerships, and consulting as revenue streams. The company’s products work by providing large, adaptable AI models that clients can license or customize for specific needs, enabling faster processing and efficient inference compared to traditional designs. Cartesia differentiates itself from competitors by using subquadratic, state space approaches instead of relying mainly on standard Transformer models, and by combining open-source releases with enterprise partnerships. The goal is to help businesses and research institutions access powerful, adaptable AI capabilities while building a community around its technology and sustaining revenue through licensing and services.

Company size
51-200
Company stage
Late Stage VC
Total funding
$191M
Headquarters
San Francisco, CA
Founded
2023

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