Audio Data Engineer – Speech Cleaning & Pipeline Automation (TTS)

About Us

Hippocratic AI is developing the first safety-focused Large Language Model (LLM) for healthcare. Our mission is to dramatically improve healthcare accessibility and outcomes by bringing deep healthcare expertise to every person. No other technology has the potential for this level of global impact on health.

Why Join Our Team

• Innovative mission: We are creating a safe, healthcare-focused LLM that can transform health outcomes on a global scale.

• Visionary leadership: Hippocratic AI was co-founded by CEO Munjal Shah alongside physicians, hospital administrators, healthcare professionals, and AI researchers from top institutions including El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, Meta, Microsoft and NVIDIA.

• Strategic investors: Raised $137 million from top investors including General Catalyst, Andreessen Horowitz, Premji Invest, SV Angel, NVentures (Nvidia Venture Capital), and Greycroft.

• Team and expertise: We are working with top experts in healthcare and artificial intelligence to ensure the safety and efficacy of our technology.

For more information, visit www.HippocraticAI.com.

We value in-person teamwork and believe the best ideas happen together. Our team is expected to be in the office five days a week in Palo Alto, CA unless explicitly noted otherwise in the job description.

About the Role

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.

Responsibilities

• 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.
  

Required Qualifications

• 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).