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 prototype agentic AI systems for clinical decision support and documentation assistance
Optimize models for real-time inference with sub-200ms latency requirements
Build rigorous evaluation frameworks for clinical accuracy, MDM validation, and MIPS quality measure compliance
Collaborate with clinical experts to validate outputs and ensure alignment with regulatory and documentation standards
Transition research findings into production-ready solutions in partnership with applied ML and platform engineering teams
Required Qualifications
5+ years of experience in machine learning research or ML engineering with a focus on NLP and/or speech recognition
Strong expertise in PyTorch or TensorFlow
Deep experience with transformer architectures and large language models
Proven ability to design and build production-grade ML pipelines at scale
Strong understanding of model optimization techniques (quantization, distillation, pruning)
Experience working with cloud ML platforms (AWS SageMaker, GCP Vertex AI, or equivalent)
Master’s or PhD in Computer Science, Machine Learning, or related field
Preferred Qualifications
Experience in healthcare AI or clinical NLP
Familiarity with medical terminology and clinical documentation workflows
Experience with speech recognition systems (Whisper, Conformer architectures, etc.)
Knowledge of medical coding systems (CPT, ICD-10, SNOMED)
Publications in leading ML/NLP conferences
Experience deploying models in regulated environments (e.g., GovCloud, HIPAA-compliant systems)
Technical Environment
Python, PyTorch, TensorFlow
Transformer-based LLM architectures
Triton Inference Server (AWS GovCloud deployments)
AWS (SageMaker, EKS, S3, Lambda)
Real-time inference systems with strict latency constraints (<200ms)
Clinical data pipelines and structured evaluation frameworks
Compensation & Benefits
Meaningful equity compensation
Unlimited PTO
Premium health, dental, and vision coverage
401(k) plan
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