LLM Engineer

• Develop and optimize models generating clinical documentation, including SOAP notes and specialty-specific note formats
• Build models for medication extraction, orders, ICD-10 coding, E&M coding, patient visit summaries, and other structured clinical artifacts
• Apply structured generation, tool use, and agentic approaches for reliable, controllable clinical outputs
• Evaluate open-weight and proprietary models and determine when fine-tuning, distillation, structured generation, or task-specific models outperform general-purpose API approaches
• Fine-tune and post-train open-weight LLMs on proprietary clinical datasets using SFT, distillation, preference optimization, and reinforcement learning
• Research methods to reduce inference cost and latency while maintaining or improving clinical quality
• Serve and optimize open-weight models in production at scale
• Build evaluation frameworks for clinical accuracy, hallucinations, completeness, formatting, and clinician preferences
• Build datasets, benchmarks, and evaluation infrastructure for measurable, reproducible model improvements
• Design experiments demonstrating impact on real-world clinical outcomes
• Move research from idea through dataset, experiment, evaluation, and production
• Deploy successful research approaches into production
• Balance model quality with latency, inference cost, reliability, and scalability
• Collaborate with clinicians, speech and applied ML engineers, and platform engineers

Explore ML Engineer, LLM at Knowtex. Review requirements and application details.