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Cartesia

Research Engineer, Audio Understanding

*HQ - San Francisco, CA$200k–$350kfulltimemidAdded 3 weeks ago

About this role

Cartesia is seeking a Research Engineer to lead data strategy for multilingual AI model training. You'll design large-scale datasets, build evaluation systems, and ensure models perform well across dozens of languages while mitigating bias and improving data quality.

What you'll do

  • Design and build large-scale multilingual datasets for model training with controlled experiments to measure performance impact
  • Develop evaluation frameworks for speech models using both manual annotation and automated metrics at scale
  • Implement data steering techniques to improve model intelligence and reduce bias
  • Build automated quality control systems to validate and filter generated data
  • Partner with product teams to ensure support for key languages and markets
  • Guide human annotation and evaluation processes across multiple languages

What they're looking for

  • Experience with large multilingual datasets
  • Generative models (speech, text, or multimodal)
  • Applied machine learning with data-centric focus
  • Human annotation and evaluation guidance
  • Scalable systems design bridging research and production
  • Linguistic nuance and cultural understanding
  • Data quality and bias mitigation
  • Python or similar programming languages

Benefits

  • Competitive base salary with equity package
  • Fully covered medical, dental, and vision insurance for you and family
  • Parental leave (12 weeks maternity, 9 weeks paternity)
  • 401(k), commuter allowance, flexible PTO, and daily meals/snacks
  • In-person collaboration in San Francisco, London, or Bangalore offices
  • Visa sponsorship support available
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Cartesia

Cartesia builds multimodal AI models and voice AI platforms that power enterprise applications. The company is hiring for roles spanning customer support, enterprise deployments, inference infrastructure, internal developer tooling, and forward-deployed engineering to scale their AI solutions across production environments.

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Likely interview questions

  • Walk us through your experience building or working with large multilingual datasets. What languages have you worked with, and what were the key challenges you encountered?
  • Describe a time when you used data-centric approaches to improve model performance. How did you measure the impact of your changes?