ML Engineer, LLM

Knowtex

. Develop and optimize models generating clinical documentation, including SOAP notes and specialty-specific note formats .

Posted 10/7/2026full-timeSan Francisco • California • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in fine-tuning and deploying large language models (LLMs) for clinical documentation and healthcare applications, with a strong focus on experimental methodology and model evaluation. Proficient in translating research into production systems while optimizing for quality, cost, and scalability.

Highest-signal resume keywords

Fine-Tuning Open-Weight LLMsPython ProgrammingPyTorch FrameworkLarge-Scale Dataset ManagementLLM Evaluation and Benchmarking

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning ResearchLarge Language ModelsTransformer ArchitecturesExperimental MethodologyStructured GenerationReinforcement LearningDistillation TechniquesPreference OptimizationClinical DocumentationICD-10 Coding

Soft Skills

Independent Research DesignCollaboration with CliniciansCommunication Skills

Tools & Technologies

SFTVLLMTensorRT-LLMQuantizationSpeculative Decoding

Industry Keywords

Healthcare AIClinical NLPHIPAA ComplianceMedical TerminologyCPT CodingE&M CodingSNOMED

Tech Stack

Tools & technologies

PythonPyTorchSOAP

About the role

Key responsibilities & impact

Develop and optimize models generating clinical documentation, including SOAP notes and specialty-specific note formats
Build models for 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 for reliable, controllable clinical outputs
Evaluate open-weight and proprietary models and determine when fine-tuning, distillation, structured generation, 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 methods to reduce inference cost and latency while maintaining or improving clinical quality
Serve and optimize open-weight models in production at scale
Build evaluation frameworks for clinical accuracy, hallucinations, completeness, formatting, and clinician preferences
Build datasets, benchmarks, and evaluation infrastructure for measurable, reproducible model improvements
Design experiments demonstrating impact on real-world clinical outcomes
Move research from idea through dataset, experiment, evaluation, and production
Deploy successful research approaches into production
Balance model quality with latency, inference cost, reliability, and scalability
Collaborate with clinicians, speech and applied ML engineers, and platform engineers

Requirements

What you’ll need

Must be authorized to work in the U.S.; visa sponsorship is unavailable
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, including SFT, distillation, preference optimization, or reinforcement learning
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 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: experience building LLM evaluation systems, including LLM-as-judge, human preference, and task-specific benchmarks
Preferred: experience serving and optimizing open-weight models at scale, including vLLM, TensorRT-LLM, quantization, or speculative decoding
Preferred: experience with structured generation, tool use, or agentic systems
Preferred: experience in healthcare AI or clinical NLP
Preferred: familiarity with clinical documentation workflows and medical terminology
Preferred: knowledge of ICD-10, CPT, E&M, or SNOMED
Preferred: publications at leading ML or NLP conferences
Preferred: experience deploying ML systems in HIPAA-compliant or regulated environments
Preferred: experience in fast-moving startups where researchers own projects from experimentation through production

Benefits

Comp & perks

Meaningful equity compensation
Unlimited PTO
Premium health, dental, and vision coverage
401(k) plan
Hybrid work model, in person Monday–Wednesday in the San Francisco office