Research Scientist, Speech Technologies
Job in
Palo Alto, Santa Clara County, California, 94306, USA
Listed on 2026-07-03
Listing for:
Dormont Manufacturing Co
Full Time
position Listed on 2026-07-03
Job specializations:
-
IT/Tech
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist
Job Description & How to Apply Below
About Us:
Hippocratic AI is building safety-focused large language model (LLM) for the healthcare industry. Our team comprised of ex-researchers from Microsoft, Meta, Nvidia, Apple, Stanford, John Hopkins and Hugging Face are reinventing the next generation of foundation model training and alignment to create AI-powered conversational agents for real time patient-AI interactions.
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.
Responsibilities:- Design, Develop, Evaluate and update data-driven models for Speech First applications.
- Participate in Research activities including the application and evaluation of speech technologies in the medical domain.
- Research and implement 0 to 1 SOTA models for conversational speech recognition.
- PhD with 3+ years of experience in Speech Recognition or related field or Masters with 5+ years of hands on experience with ASR.
- Experience Designing and developing algorithms for accurate and efficient speech recognition for both Streaming and Non-Streaming use cases.
- Experience with Training, evaluating, and optimizing ASR models for various factors including accuracy, latency, and resource utilization.
- Experience with Preprocessing and curating large speech datasets for training models.
- Strong programming skills with working knowledge of Python & C++
- Comfort working in a Linux/ Unix command-line environment.
- Team player with good communication skills (oral and written)
- Experience with building 0 to 1 ASR solutions, including setting up data pipelines, SOTA model architectures and evaluation pipelines.
- Hands-on Experience with ESPNET, Kaldi and Pytorch.
- Experience with CUDA.
- Experience with leveraging LLMs for enhanced speech recognition tasks.
- Experience with Neural/ E2E End pointer modeling.
- Publications in tier 1 journals in the field of speech recognition/ NLP.
For more information, visit
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