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Real-time voice AI models and a platform to build voice agents.

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About Cartesia

The company, in brief.

What they do

Cartesia does research on AI models and ships real-time voice models. Sonic turns text into speech, Ink turns speech into text, and Managed Agents builds voice agents. The founders met as PhD students at the Stanford AI Lab, where they invented State Space Models (SSMs), a model design for fast, efficient AI. Index Ventures and Lightspeed back it.

What they value

  • Get the fundamentals right.
  • Build to solve problems.
  • Aim beyond what is possible.
  • Be honest, with ourselves and others.
  • Have fun with it.
  • Ship fast, with a high bar for quality and design.

Where they hire

Cartesia is an in-person team. Of 30 open roles, 26 are in San Francisco, 2 in London, and 2 in Bangalore. No role is remote. Every Posting says that Cartesia gives visa sponsorship support case by case, but cannot always promise success. US Postings say that it uses E-Verify.

Teams and roles

The board lists 30 open roles.

  • Research (10): researchers for models, audio, evals, and post-training, plus inference and data infrastructure engineers.
  • Engineering (7): product engineers, an agent harness engineer, enterprise and forward deployed engineers, an analytics engineer, and engineering managers.
  • GTM (6): account executives, account and customer success managers, partnerships, and GTM strategists.
  • People (4) and Product operations (3): recruiting, workplace operations, an executive business partner, product support, and research operations.

US pay ranges are from $100K to $375K, and most roles include equity.

What they look for

Most Postings ask for a set number of years in the field, from 2 years in sales development to 8 years in partnerships. Many roles want experience at a high-growth startup. Many Postings ask you to own work end to end and to communicate clearly with technical and non-technical people. Product engineering Postings value what you shipped more than your years of experience. Researchers need deep expertise in generative models and large-scale training.

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