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.

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

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

ML Engineer, LLM's

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Knowtex

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