Role description
Audio Transcription Specialist
Hippocractic AIRole mission
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.
What you will accomplish
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.
The team
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 team moves fast, operates with high autonomy, and turns quality findings directly into improvements to data and models.
What you will do
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.
Validate audio data pipelines: Test audio processing and annotation pipelines end to end, and verify that the training data they produce is accurate, consistent, and aligned with human judgments.
Investigate production errors: Analyze audio and transcription errors observed in production ASR systems, document failure patterns, and assess their impact on downstream performance.
Build useful error categories: Develop and maintain an error taxonomy that researchers use to identify ASR weaknesses, prioritize improvements, and measure progress.
Collaborate across teams: Present quality findings to researchers, engineers, and external providers, and partner with them to turn those findings into changes to data and systems.
Basic qualifications
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
Ability to work onsite in our Menlo Park office five days a week
Preferred qualifications
Familiarity with ASR systems and evaluation metrics such as word error rate (WER)
Experience evaluating speech across accents, dialects, noisy environments, or multiple languages
Experience working with external annotation providers or research and engineering teams
Experience identifying recurring quality issues and improving review processes
Familiarity with spreadsheets, SQL, or scripting tools for analyzing quality trends
Experience with healthcare audio or other privacy-sensitive, regulated data
The base pay range for this position is $30–$32 per hour. The final rate will be based on relevant experience and qualifications.
Why Join Hippocratic AI
Reinvent healthcare with AI that puts safety first. We’re building the world’s first healthcare‑only, safety‑focused LLM — a breakthrough platform designed to transform patient outcomes at a global scale. This is category creation.
Work with the people shaping the future. Hippocratic AI was co‑founded by CEO Munjal Shah and a team of physicians, hospital leaders, AI pioneers, and researchers from institutions like El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, Meta, Microsoft, and NVIDIA.
Backed by the world’s leading healthcare and AI investors. We recently raised a $126M Series C at a $3.5B valuation, led by Avenir Growth, bringing total funding to $404M with participation from CapitalG, General Catalyst, a16z, Kleiner Perkins, Premji Invest, UHS, Cincinnati Children’s, WellSpan Health, John Doerr, Rick Klausner, and others.
Build alongside the best in healthcare and AI. Join experts who’ve spent their careers improving care, advancing science, and building world‑changing technologies — ensuring our platform is powerful, trusted, and truly transformative.
Equal Opportunity
Hippocratic AI is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, sex, age, disability, sexual orientation, gender identity or expression, genetic information, military or veteran status, or any other characteristic protected by applicable law. We are committed to building a team that reflects the patients we serve. We actively encourage applications from candidates of all backgrounds. If you require accommodations during the hiring process, please contact people@hippocraticai.com.
Please be aware of recruitment scams impersonating Hippocratic AI. All recruiting communication will come from @hippocraticai.com email addresses. We will never request payment or sensitive personal information during the hiring process.