Hippocratic AI builds the safest generative AI healthcare agent for health systems, payors, and pharma. Over 180 million clinical interactions across 1,000+ use cases with 60+ partners worldwide. Backed by General Catalyst, Kleiner Perkins and a16z.
About the role
Evaluate external transcription quality: Review transcriptions from external providers against annotation guidelines for accuracy and completeness, deliver written feedback to each provider, and track recurring error patterns over time. Improve quality processes: Design, document, and refine QA workflows, review criteria, and transcription guidelines, and implement process changes that close systematic quality gaps identified in provider output. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Bachelor's degree in Linguistics, Speech and Language Sciences, or a related field
- 2+ years of experience in transcription, linguistic annotation, audio quality assurance, or a related field
- Strong listening skills and attention to detail, including the ability to identify subtle transcription errors
- Demonstrated ability to apply annotation guidelines consistently and exercise sound judgment in ambiguous cases
- Strong written communication skills, with the ability to explain errors and provide actionable feedback
- Comfort working with sensitive audio data and following data privacy and handling requirements
More about this role
As HAI's Audio Transcription Specialist, you will own the quality of the transcription data behind our speech systems, directly shaping how accurately our AI hears and understands patients and clinicians. This role exists because as our voice agents scale across health systems, the quality of our audio training data and our ability to explain ASR failures now determine how fast and how safely our speech systems improve.
Within 90 days: Stand up a repeatable QA process for external transcription providers, with refreshed review criteria and a first error taxonomy that researchers use to prioritize ASR work.
Within 12 months: Measurably reduce recurring transcription errors, with provider feedback loops, validated audio pipelines, and a maintained error taxonomy that researchers rely on to guide and measure ASR improvements.
What success looks like: Your work will improve transcription consistency, reduce recurring data quality issues, and give researchers clear, well-categorized evidence to guide ASR improvements.
You will work closely with researchers and engineers who build and evaluate our speech systems, and with the external providers who produce our transcription data. The...
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