Audio Transcription Specialist

Role 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