Engineering Manager, Data

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.

Your Impact

• Lead and grow a high-caliber team of data infrastructure engineers and data-focused researchers, splitting focus between shared infra and dedicated modeling teams.
• Set the technical direction for how Cartesia processes, stores, and serves multimodal data at web scale.
• Drive the creation of SOTA multimodal pre-training datasets — including the annotation models and curation strategies that determine what our models learn.
• Establish a rigorous, empirical approach to data quality: training and evaluating models specifically to understand what makes data valuable, and feeding that back into dataset decisions.
• Partner closely with our modeling and research teams to make sure 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.

What You Bring

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

Nice-To-Haves

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