ML Scientist (Research)

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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About this role

ML Scientist (Research) San Francisco | Hybrid | Remote About Knowtex Knowtex is building the future of voice AI operating systems for clinicians, transforming how healthcare documentation happens at the point of care. Founded by Stanford AI scientists with deep clinical experience, we're experiencing explosive growth across both commercial health systems and federal healthcare, with our ambient documentation platform scaling rapidly to thousands of clinicians across hundreds of specialties. We're at an inflection point where cutting-edge AI meets real clinical impact, giving clinicians hours back each day to focus on what matters most - their patients. Position Overview We are seeking an ML Scientist (Research) to advance Knowtex's voice AI and clinical NLP capabilities at the frontier of healthcare AI. This role focuses on developing and evaluating novel machine learning approaches for medical speech recognition, clinical language understanding, and agentic AI systems tailored for healthcare environments. You will work on research-driven initiatives that directly impact our ambient documentation platform, collaborating closely with applied ML and engineering teams to transition validated research into scalable production systems. This role reports to the CTO and plays a central part in defining the next generation of clinical AI infrastructure. Key Responsibilities - Develop and optimize models for medical speech recognition across 200+ specialties - Research and implement clinical NLP pipelines for automated E&M coding and ICD-10 classification - Design and evaluate note quality scoring systems using LLMs and structured clinical rubrics - Create specialty-specific language models (e.g., gastroenterology, dermatology, emerging markets) - Design and

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