ML Engineer, LLM's

About the role

Job title: ML Engineer / Researcher, Large Language Models

WORK AUTHORIZATION Must be authorized to work in the U.S.; we are unable to sponsor visas for this role.

About Knowtex Knowtex is building the future of voice AI operating systems for clinicians, transforming how healthcare documentation happens at the point of care. We are growing fast across both commercial health systems and federal healthcare, and our ambient documentation platform is scaling to thousands of clinicians across hundreds of specialties.

We are at an inflection point where advances in language models and clinical AI can fundamentally change how clinicians interact with technology. That gives them more time to focus on what matters most: their patients.

Position Overview We are hiring an ML Engineer / Researcher focused on Large Language Models to help build the next generation of Knowtex's AI stack.

You will develop and optimize the models behind our clinical documentation and structured clinical reasoning, improving quality, cost, latency, and control. Your work will draw on Knowtex's large proprietary clinical dataset, built from real-world encounters across hundreds of specialties.

This is a research-heavy role with a direct path to production. You will design experiments, build datasets and evaluation systems, and fine-tune and post-train models. You will also work closely with engineering and clinical teams to deploy successful approaches at scale. The role plays a central part in defining Knowtex's long-term LLM strategy.

Key Responsibilities

Clinical Documentation & Reasoning

- 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

Model Strategy & Training

- 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

Evaluation

- 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