ML Engineer, LLM's
About the role
ML Engineer / Researcher focused on Large Language Models at Knowtex, building the next generation of the AI stack for clinical documentation and reasoning. Research-heavy role with path to production, working with proprietary clinical data and cross-functional teams in a hybrid SF office.
Responsibilities
- 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
- 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
- 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
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
Skills
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)
About Knowtex
Making doctors 2x more efficient with AI note-taking & charge capture
We’re on a mission to leverage AI and voice technology to solve inefficiencies and revenue leakage in our healthcare system. With the rise of EHRs, the manual documentation burden on doctors has never been higher, leading to burnout and fatigue and high attrition rates. Assigning the correct diagnosis and billing codes is crucial for proper reimbursement and patient care, but medical coding is becoming more and more complex each year with growing numbers of codes (over 145,000 now), and doctors are unsupported by current technology to keep up with its ever increasing requirements. $125 billion is left on the table each year by healthcare organizations due to improper documentation and coding. Knowtex creates visit notes with up-to-date and accurate reimbursement codes from doctor-patient conversations, allowing the doctor to focus on patient care and preventing revenue leakage for hospital systems through standardized, transparent, and accurate documentation.
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