Hippocratic AI is the leading generative AI company in healthcare. We have the only system that can have safe, autonomous, clinical conversations with patients. We have trained our own LLMs as part of our Polaris constellation, resulting in a system with over 99.9% accuracy.
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.
Location Requirement
We believe the best ideas happen together. To support fast collaboration and a strong team culture, this role is expected to be in our Palo Alto office five days a week, unless otherwise specified.
Hippocratic AI is seeking a skilled Audio Data Engineer to help us scale and improve our speech datasets for use in Text-to-Speech (TTS) and speech synthesis systems. In this role, you will clean and enhance real-world audio data, build automation pipelines for processing, and ensure our voice models are trained on the highest quality inputs. This work will directly shape the clarity and expressiveness of the voices used in healthcare AI applications.
Clean, denoise, and enhance large volumes of recorded speech data for use in TTS and voice synthesis pipelines.
Build and maintain automated audio preprocessing pipelines using scripting tools and open-source libraries.
Apply techniques such as background noise removal, silence trimming, gain normalization, and sample rate conversion.
Integrate tools like ffmpeg, sox, or Python-based scripts (pydub, torchaudio, librosa) into scalable workflows.
Collaborate with ML researchers and speech scientists to deliver high-quality, ready-to-train datasets.
Evaluate audio quality using perceptual and quantitative metrics, and maintain audio QA checklists.
Must-Have:
Strong experience with speech/audio cleaning using tools such as iZotope RX, Audacity, Adobe Audition, or SoX.
Proficiency in Python and audio-related scripting for automation and batch processing.
Familiarity with digital audio principles, including sample rates, bit depth, frequency bands, and compression artifacts.
Experience designing or operating scalable, automated workflows for handling audio at volume.
Meticulous attention to detail in audio quality control and error spotting.
Nice-to-Have:
Experience working on TTS model pipelines (e.g., Tacotron, VITS, FastSpeech) or speech synthesis datasets.
Background in audio engineering, phonetics, or signal processing.
Familiarity with real-time or low-latency audio processing constraints.
Experience with cloud platforms and tools for automation (e.g., AWS, Airflow, or containerized audio workflows).
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.
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