ML Engineer, LLM's

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

AWS

Python, PyTorch

Open-weight and frontier language models

Large-scale clinical text and transcript datasets

Distributed model training and inference

GPU-based model serving and optimization

Real-time clinical AI pipelines

Structured clinical evaluation and benchmarking infrastructure

Compensation & Benefits

Competitive salary

Meaningful equity compensation

Unlimited PTO

Premium health, dental, and vision coverage

401(k) plan

Work model: Hybrid, in person (M-W in our SF office)