Job description

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)

Requirements

Must-haves

Job title: ML Engineer / Researcher, Large Language Models
Must be authorized to work in the U.S.; we are unable to sponsor visas for this role.
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
Design experiments that clearly show whether an approach improves real-world clinical outcomes

Nice-to-haves

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

Application prep

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Keywords to mirror

mlaillmapimachine learningpythonaws

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto