ML Engineer, LLM's

Knowtex is building voice AI operating systems for clinicians to transform healthcare documentation at the point of care. The ML Engineer / Researcher will develop, fine-tune, evaluate, optimize, and deploy large language models for clinical documentation, structured clinical reasoning, and related healthcare AI tasks while collaborating with engineering and clinical teams.

Responsibilities:

• Develop and optimize models that generate high-quality clinical documentation, including SOAP notes and specialty-specific note formats
• Build models for downstream clinical tasks such as 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 to produce reliable, controllable clinical outputs
• Evaluate open-weight and proprietary models, and determine where fine-tuning, distillation, structured generation, or task-specific models can beat general-purpose API-based approaches
• Fine-tune and post-train open-weight LLMs on Knowtex's proprietary clinical datasets, using SFT, distillation, preference optimization, and reinforcement learning
• Research ways to reduce inference cost and latency while maintaining or improving clinical quality
• Serve and optimize open-weight models in production at scale
• Build rigorous evaluation frameworks for clinical accuracy, hallucinations, completeness, formatting, and clinician preferences
• Build datasets, benchmarks, and evaluation infrastructure that make model improvements measurable and reproducible
• Design experiments that clearly show whether an approach improves real-world clinical outcomes
• Move quickly from idea → dataset → experiment → evaluation → production
• Take successful research beyond prototypes and help deploy models into production
• Balance model quality with latency, inference cost, reliability, and scalability
• Collaborate closely with clinicians, speech and applied ML engineers, and platform engineers