Updated 19h ago

ML Engineer, LLM's at Knowtex · AI Job Signal

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

Research to Production

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

Required Qualifications

4+ years of experience in machine learning research or ML engineering, with deep expertise in large language models
Hands-on experience fine-tuning or post-training open-weight LLMs (e.g., SFT, distillation, preference optimization, RL)
Strong Python and PyTorch skills
Deep understanding of modern transformer architectures and LLM training techniques
Strong experimental methodology and the ability to independently design and execute research projects
Experience working with large-scale datasets and distributed training environments
Strong understanding of 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

Experience building LLM evaluation systems, including LLM-as-judge, human preference, and task-specific benchmarks
Experience serving and optimizing open-weight models at scale (e.g., vLLM, TensorRT-LLM, quantization, speculative decoding)
Experience with structured generation, tool use, or agentic systems
Experience in healthcare AI or clinical NLP
Familiarity with clinical documentation workflows and medical terminology
Knowledge of coding systems such as ICD-10, CPT, E&M, or SNOMED
Publications at leading ML or NLP conferences
Experience deploying ML systems in HIPAA-compliant or regulated environments
Experience in fast-moving startups where researchers own projects from experimentation through production

Technical Environment