Language Lead - Japanese (Contract)

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. Overview We are seeking native-level Japanese speakers with a linguistics background to serve as the primary language resource for voice AI in their language. You will be the trusted point of contact for all questions, quality decisions, and language-specific work related to Japanese. This is not a standard annotation engagement. You will own your language end to end, which includes managing annotation quality, auditing data, monitoring product performance, investigating customer-reported issues, and executing projects that require deep linguistic expertise and attention to detail. You will operate independently across these workstreams and serve as the team's go-to resource for anything related to your language. This is an ongoing contractor engagement. Availability during some US Pacific business hours is preferred to allow for team syncs and real-time support. About the Engagement You will coordinate with the Human Evals Manager on project deliverables and timelines. Some examples of what your day-to-day work may include: Answer questions from annotators about guidelines, edge cases, or language-specific issues via an agreed project communication channel. Cascade new feedback, instructions, or rubric updates to annotators working on your projects. Listen to annotator-rated audio files and check whether their ratings and written feedback align with the rubric. Prepare written quality assessments for annotators with specific examples and recommendations. Listen to TTS output and provide ground truth assessments of pronunciation, prosody, and naturalness in your language. Update or expand sections of the annotation guidelines when you find gaps or edge cases. Prep and run calibration meetings with annotators. Hold office hours on a mutually agreed schedule during active studies so annotators can ask questions in real time. Develop onboarding materials and resources for new annotators. Write QA reports on annotation quality and calibration outcomes. Review production audio in your language and flag quality issues to the team. Investigate language-specific issues reported by customers and identify root caus