About the role
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 The Data Strategy Team reports directly to Cartesia's founders and shapes our models capabilities. We work alongside researchers to find where models break, through evals and direct customer conversations, and then fix them by sourcing high quality data. We're looking for people who bring technical depth, operational rigor, and high agency, who want to have a lot of impact. As a Data Strategy Lead , you'll have broad ownership and autonomy to shape one of the most important inputs to frontier AI models. You'll work across research, product, and go-to-market teams, make decisions with incomplete information, and build new processes from the ground up. If you enjoy solving ambiguous problems, learning quickly, and creating leverage through systems, you'll thrive in this role. Your Impact Partner tightly with research and modeling teams to find where models fail, through evals and direct customer conversations. Design, source, and maintain the evals that define quality for our models. Ground them in observed customer failures, benchmark continuously against other systems, and turn the results into concrete data
requirements. Establish new sources of human data to advance model performance, building collection pipelines from scratch, and own data quality from acquisition through delivery. Stay close to the research. Read papers, understand what datasets move which capabilities, and collaborate with
