8 hours ago
San Francisco, CA, USAMid Level
Responsibilities
- Develop and optimize LLMs for clinical documentation, SOAP notes, specialty-specific formats, and structured clinical artifacts.
- Fine-tune and post-train open-weight LLMs using supervised fine-tuning, distillation, preference optimization, and reinforcement learning.
- Build datasets, benchmarks, evaluation frameworks, and infrastructure for clinical accuracy, hallucination, completeness, formatting, and clinician-preference evaluation.
- Design experiments and translate successful research approaches into reliable production systems.
- Serve and optimize open-weight models at scale while balancing quality, latency, inference cost, reliability, and scalability.
- Collaborate with clinicians, speech and applied ML engineers, and platform engineers.
Requirements
- 4+ years of experience in machine learning research or ML engineering with deep large-language-model expertise.
- Hands-on experience fine-tuning or post-training open-weight LLMs.
- Strong Python and PyTorch skills.
- Deep understanding of modern transformer architectures and LLM training techniques.
- Strong experimental methodology and ability to independently design and execute research projects.
- Experience with large-scale datasets, distributed training environments, LLM evaluation, and benchmarking.
- Ability to translate research results into production systems.
- Bachelor's, Master's, or PhD in Computer Science, Machine Learning, or a related technical field, or equivalent research experience.
- Preferred qualifications include LLM evaluation systems, LLM serving and optimization, structured generation, tool use, agentic systems, healthcare AI, clinical NLP, medical terminology, clinical coding systems, leading ML or NLP publications, HIPAA-compliant or regulated deployments, and startup research-to-production experience.
Benefits
- Competitive salary and meaningful equity compensation.
- Unlimited PTO.
- Premium health, dental, and vision coverage.
- 401(k) plan.
- Hybrid work model with in-person work Monday through Wednesday at the San Francisco office.
Categories
About Knowtex
Knowtex builds an ambient voice AI platform that captures clinician‑patient conversations and generates pre‑filled clinical notes with coding suggestions, integrating into EHR workflows. It sells to health systems and federal healthcare organizations to cut documentation time and improve billing accuracy. Privately held and based in San Francisco, the company was founded by Stanford-trained AI scientists and focuses on integrations across specialties and large provider environments.