layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - ML Engineer, LLM's at Knowtex, San Francisco, CA

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

ML Engineer, LLM's

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