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Knowtex is building voice AI operating systems for clinicians, transforming healthcare documentation at the point of care. The company is scaling its ambient documentation platform across hundreds of specialties in commercial health systems and federal healthcare.
You will join as an ML Engineer / Researcher focused on Large Language Models, developing and optimizing models that power clinical documentation and structured clinical reasoning. This is a research-heavy role with a direct path to production, where you will design experiments, build datasets and evaluation systems, fine-tune and post-train models, and work closely with engineering and clinical teams to deploy approaches at scale. The role is central to defining Knowtex's long-term LLM strategy.
Key responsibilities include:
- Develop and optimize models for high-quality clinical documentation (SOAP notes, specialty-specific formats) and downstream clinical tasks (medication extraction, orders, ICD-10 coding, E&M coding, patient visit summaries, structured clinical artifacts)
- Apply structured generation, tool use, and agentic approaches to produce reliable, controllable clinical outputs
- Evaluate open-weight and proprietary models to determine where fine-tuning, distillation, or task-specific models outperform general-purpose API approaches
- Fine-tune and post-train open-weight LLMs on 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 demonstrate whether an approach improves real-world clinical outcomes
- Move quickly from idea through dataset, experiment, evaluation, to production deployment
- Balance model quality with latency, inference cost, reliability, and scalability
- Collaborate closely with clinicians, speech and applied ML engineers, and platform engineers
The technical environment includes 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, and structured clinical evaluation and benchmarking infrastructure.
Work model is hybrid, in-person Monday-Wednesday in the San Francisco office.
REQUIREMENTS:
- 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 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 related technical field, or equivalent research experience
PREFERRED QUALIFICATIONS:
- Experience building LLM evaluation systems (LLM-as-judge, human preference, task-specific benchmarks)
- Experience serving and optimizing open-weight models at scale (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 (ICD-10, CPT, E&M, 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
