Clinical Informaticist

About Knit Health

Knit Health is building a novel clinical foundation model to improve how healthcare is delivered. We combine expertise in AI with deep clinical knowledge to develop safe, trustworthy systems that improve care, expand access, and reduce waste.

Knit is led by a founding team with roots at the University of California, Berkeley. The team has developed a novel AI architecture designed to reason like physicians. We are now using that foundation model, together with frontier clinical LLMs, to build a next-generation clinical intelligence platform for healthcare providers.

We are venture backed and work with U.S.-based health systems and data providers.

The role

At the center of Knit's technology is the Knit Data Model (KDM), which transforms complex healthcare data into a structured representation that our clinical foundation model can learn from.

We're hiring a Clinical Informaticist to help answer a fundamental question: How do we know whether our data model accurately captures the clinical information that matters most?

You'll lead the design and execution of our data model evaluation strategy, developing rigorous methods to measure how well clinical information is represented, identifying where meaningful information may be lost, and recommending improvements.

In your first six months, you'll establish a robust evaluation framework for our clinical data model, identify meaningful opportunities for improvement, and collaborate with the team to translate your findings into concrete changes to the model.

This is a hands-on individual contributor role with significant ownership and influence. You'll work closely with clinicians, data engineers, data scientists, and AI researchers to shape how clinical information is represented and used across our technology.

You'll report to the Senior Director of Data Science.

What you'll do

Lead clinical data model evaluation

- Design and implement a rigorous evaluation framework to assess the clinical accuracy, completeness, and representativeness of our data model.

- Define meaningful evaluation metrics, develop analytical methodologies, and measure performance across data model releases.

- Independently design and drive analyses of large clinical datasets, using available analytical and AI-assisted tools to execute evaluations, interpret results, and identify opportunities to improve how clinical information is represented.

- Translate evaluation findings into evidence-based recommendations and prioritized improvements.

Guide clinical data representation and integration

- Provide clinical informatics expertise as we incorporate new data sources and modalities, including clinical notes, claims, and other structured and unstructured healthcare information.

- Evaluate clinical terminology mappings, data granularity, provenance, and information loss during transformation.

- Help determine which clinical concepts and details are essential to preserve for downstream AI applications.

- Collaborate with data science teams to develop approaches for monitoring clinical data model performance.

Partner across clinical, engineering, and AI teams

- Collaborate with data engineers to evaluate data transformation logic, clinical concept mapping, and data model implementation.

- Assess how clinical information is organized, grouped, and represented, identifying potential gaps or inaccuracies.

- Work with clinicians and researchers to translate clinical expertise into measurable data model requirements.

- Communicate complex analytical findings to technical and clinical stakeholders, challenge assumptions, and advocate for improvements supported by evidence.

Support customer and partner implementations

- Provide clinical informatics guidance as Knit integrates its data model with new customer and partner data environments.

- Evaluate new clinical concepts and data sources to ensure appropriate representation and consistency.

- Partner with implementation and engineering teams to address clinical data modeling challenges.

What we are looking for

- Clinical informatics expertise: Strong understanding of clinical data systems, healthcare terminology, and how information is represented across electronic health records, claims, or related healthcare datasets.

- Research and evaluation: Experience independently developing research questions, designing evaluation frameworks, and translating findings into actionable recommendations.

- Technical capability: Ability to read and understand SQL and Python, clearly define analytical approaches, leverage AI-assisted tools to execute analyses, and critically evaluate results.

- Clinical terminology knowledge: Working familiarity with relevant coding standards such as ICD-10, SNOMED CT, LOINC, RxNorm, or related systems.

- Communication and influence: Ability to work effectively across clinical and technical teams, defend analytical conclusions, and translate findings into actionable recommendations.

- Independent problem-solving: Comfort tackling ambiguous, open-ended problems where established methodologies may not yet exist.

- Relevant experience: Professional or research experience in clinical informatics, healthcare data science, biomedical informatics, or a closely related field. We value demonstrated expertise and ownership over a specific number of years.

Nice-to-haves

- Advanced training in biomedical or clinical informatics, including a relevant master's degree or PhD.

- Clinical training (MD, DO, RN, PharmD, or similar) or sustained collaboration with practicing clinicians.

- Experience building or evaluating clinical data models, terminology mappings, or ontology systems, including OMOP or UMLS.

- Experience developing research methodologies, evaluating clinical data quality, or quantitatively assessing information loss.

- Familiarity with clinical foundation models, transformer architectures, or complex analysis using longitudinal healthcare data.

- Experience working with multi-source healthcare datasets, including EHRs, claims, registries, or health information exchanges.

- Experience reviewing clinical data transformation pipelines or working with large-scale data processing systems.

Salary Range

Knit Health offers a competitive compensation package that includes base salary, equity, and opportunities for advancement. The starting salary range is approximately $165,000 to $200,000 per year.

Location

San Francisco, CA. Hybrid work arrangement, with two to three days per week in the office preferred.

Benefits

Generous benefits for full-time employees include: medical, dental, and vision coverage with 100% of premiums paid for employees and dependents (full coverage for dental, vision, and our Gold medical plan; employees may choose to buy up to Platinum); coverage begins on the first day of employment. Additional benefits include a 401(k) plan and 24 days of PTO annually.

Final Notes

Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice.

Knit Health is an equal opportunity employer and is committed to a diverse workplace. People from diverse racial, ethnic and cultural backgrounds, women, LGBTQ+ individuals, and persons with disabilities are highly encouraged to apply.