What the role involves
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 Applied ML Engineer to productionize and scale machine learning systems powering our voice AI platform. This role bridges research and engineering — transforming models into reliable, low-latency, production-grade systems deployed across enterprise healthcare environments.
You will work closely with ML Scientists, Backend Engineers, and Platform teams to optimize inference performance, build evaluation pipelines, and ensure robust model deployment in regulated environments.
Key Responsibilities
Productionize ML models for real-time clinical applications
Optimize inference pipelines for low latency and high throughput
Deploy and scale models using AWS-based infrastructure
Build automated evaluation and regression testing frameworks for LLM outputs
Implement monitoring systems for model performance and drift detection
Collaborate with Backend teams to integrate ML services into APIs and workflows
Improve model efficiency through quantization, batching, caching, and optimization techniques
Support specialty-level model evaluation and performance analysis
Contribute to CI/CD workflows for ML deployment
Required Qualifications
3–7+ years of experience in machine learning engineering or applied ML roles
Strong proficiency in Python and PyTorch (or TensorFlow)
Experience deploying ML models in production environments
Familiarity with transformer architectures and large language models
Experience with model optimization techniques (quantization, distillation, pruning)
Experience working with cloud infrastructure (AWS preferred)
Strong software engineering fundamentals and debugging skills
Preferred Qualifications
Experience with speech recognition systems or NLP pipelines
Experience with Triton Inference Server or similar deployment frameworks
Familiarity with healthcare data or clinical documentation workflows
Experience working in regulated environments (HIPAA, GovCloud, etc.)
Knowledge of medical coding systems (ICD-10, CPT)
Technical Environment
Python, PyTorch / TensorFlow
Transformer-based LLM architectures
AWS (SageMaker, ECS, Lambda, S3)
Triton Inference Server
CI/CD pipelines for ML deployment
Observability tools for performance and drift monitoring
Compensation & Benefits
Meaningful equity compensation
Unlimited PTO
Premium health, dental, and vision coverage
401(k) plan
About Knowtex
We’re on a mission to leverage AI and voice technology to solve inefficiencies and revenue leakage in our healthcare system. With the rise of EHRs, the manual documentation burden on doctors has never been higher, leading to burnout and fatigue and high attrition rates. Assigning the correct diagnosis and billing codes is crucial for proper reimbursement and patient care, but medical coding is becoming more and more complex each year with growing numbers of codes (over 145,000 now), and doctors are unsupported by current technology to keep up with its ever increasing requirements. $125 billion is left on the table each year by healthcare organizations due to improper documentation and coding. Knowtex creates visit notes with up-to-date and accurate reimbursement codes from doctor-patient conversations, allowing the doctor to focus on patient care and preventing revenue leakage for hospital systems through standardized, transparent, and accurate documentation.
Full Knowtex profile