Engineering Manager, 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

At Cartesia, the quality of our models is inseparable from the quality of our data. As we push the frontier of real-time multimodal AI, we're looking for a leader to own the systems, datasets, and research that make our training data exceptional.

You'll lead our Data team, responsible for three tightly connected areas:

- Data infrastructure — web-scale processing and storage that keeps petabytes of multimodal data flowing reliably to the teams that depend on it.

- State-of-the-art pre-training datasets — building the multimodal annotation models that understand who said what, when, and how, and curating the data mixes that shape model behavior.

- Data-centric modeling — training and evaluating models to understand what actually makes data good, and turning those insights into better datasets.

You'll start by managing a team of 6–8 spanning core data infrastructure and individual modeling efforts, and grow it toward ~12 as our ambitions scale. This is a role for someone who has done this before: built and led a data team, shipped data at the scale modern models demand, and knows the differenc...