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 formatsBuild models for downstream clinical tasks such as medication extraction, orders, ICD-10 coding, E&M coding, patient visit summaries, and other structured clinical artifactsApply 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 approachesFine-tune and post-train open-weight LLMs on Knowtex's proprietary clinical datasets, using SFT, distillation, preference optimization, and reinforcement learningResearch ways to reduce inference cost and latency while maintaining or improving clinical qualityServe and optimize open-weight models in production at scale Evaluation Build rigorous evaluation frameworks for clinical accuracy, hallucinations, completeness, formatting, and clinician preferencesBuild datasets, benchmarks, and evaluation infrastructure that make model improvements measurable and reproducibleDesign experiments that clearly show whether an approach improves real-world clinical outcomes Research to Production Move quickly from idea → dataset → experiment → evaluation → productionTake successful research beyond prototypes and help deploy models into productionBalance model quality with latency, inference cost, reliability, and scalabilityCollaborate 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 modelsHands-on experience fine-tuning or post-training open-weight LLMs (e.g., SFT, distillation, preference optimization, RL)Strong Python and PyTorch skillsDeep understanding of modern transformer architectures and LLM training techniquesStrong experimental methodology and the ability to independently design and execute research projectsExperience working with large-scale datasets and distributed training environmentsStrong understanding of LLM evaluation and benchmarkingAbility to translate research results into production systemsBachelor'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 benchmarksExperience serving and optimizing open-weight models at scale (e.g., vLLM, TensorRT-LLM, quantization, speculative decoding)Experience with structured generation, tool use, or agentic systemsExperience in healthcare AI or clinical NLPFamiliarity with clinical documentation workflows and medical terminologyKnowledge of coding systems such as ICD-10, CPT, E&M, or SNOMEDPublications at leading ML or NLP conferencesExperience deploying ML systems in HIPAA-compliant or regulated environmentsExperience in fast-moving startups where researchers own projects from experimentation through production Technical Environment AWSPython, PyTorchOpen-weight and frontier language modelsLarge-scale clinical text and transcript datasetsDistributed model training and inferenceGPU-based model serving and optimizationReal-time clinical AI pipelinesStructured clinical evaluation and benchmarking infrastructure Compensation & Benefits Competitive salaryMeaningful equity compensationUnlimited PTOPremium health, dental, and vision coverage401(k) planWork model: Hybrid, in person (M-W in our SF office)