Voice data for AI. Backed by Y Combinator.
About the role
Evaluate speech and conversational quality. Assess pronunciation, intelligibility, prosody, pacing, tone, and turn-taking in human recordings and model outputs. Explain why a response sounds natural, awkward, overly scripted, or inappropriate for the situation. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- A background in linguistics, speech science, phonetics, phonology, or a related field, through academic study or substantial applied experience
- Strong listening and analytical skills, with the ability to describe specific features of speech and explain how they affect meaning, clarity, and naturalness
- Experience with speech data, linguistic annotation, transcription, language assessment, or conversational analysis
- The ability to turn nuanced judgments into guidelines that other people can apply consistently
- Clear written communication and comfort giving specific, constructive feedback
- Careful judgment about ambiguity, language variation, and the difference between personal preference and a task’s actual requirements
More about this role
Besimple AI is building datasets, benchmarks, and training environments that help voice models understand people, interact naturally, and complete real tasks.
We’re looking for a linguist to help define and improve the quality of the speech and conversations used to train these models. You’ll bring a trained ear for language, an understanding of how people communicate, and the ability to explain what makes a conversation clear, natural, and appropriate to its context.
Your work will connect linguistic expertise with practical decisions: which recordings are suitable for training, how contributors should be evaluated, what annotators should label, and how we measure the behavior of conversational agents.
Evaluate speech and conversational quality. Assess pronunciation, intelligibility, prosody, pacing, tone, and turn-taking in human recordings and model outputs. Explain why a response sounds natural, awkward, overly scripted, or inappropriate for the situation.
Build quality standards and evaluation rubrics. Translate customer requirements and feedback into clear criteria, examples, and decision rules. Define what reviewers should accept, flag, or reject, including how expectations...
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