Clinical AI Informaticist

Suki•Redwood City, CA

6h•$200,000 - $220,000•Hybrid

About The Position

Suki is developing healthcare technology to combat clinician burnout using its proprietary Ambient Clinical Intelligence (ACI) platform. This platform aims to improve the lives of clinicians, patients, and the healthcare system by automating administrative tasks and reducing documentation time. The flagship product, Suki for Clinicians, assists with pre-visit, encounter, and post-visit stages, significantly reducing note-taking time and burnout. The partner product, Suki for Partners, enables other healthcare platforms to integrate Suki's ACI capabilities via APIs and SDKs. The role of a Clinical AI Informaticist is for a physician-scientist who will work at the intersection of clinical medicine, product, data science, and AI engineering. This hands-on role involves designing evaluation frameworks, curating clinical datasets, refining prompts, analyzing model outputs, and building prototypes. The ideal candidate is comfortable with both technical and clinical environments and will have significant influence over product development.

Requirements

Clinical background: MD or DO with 3+ years of direct patient care experience strongly preferred. Other advanced clinical or doctoral degrees with demonstrable expertise in clinical AI, informatics, or computational medicine will be considered.
Technical proficiency: Comfortable working in Python, SQL, or R for data analysis, regularly using tools like Jupyter notebooks and pandas.
AI/ML literacy: Working understanding of large language models, prompt engineering, and clinical NLP.
Deep familiarity with at least one major EHR system (Epic, Oracle Health/Cerner, MEDITECH, athenahealth).
Working knowledge of clinical interoperability standards (HL7 FHIR, SNOMED-CT, ICD-10, LOINC).
Scientific rigor: Experience designing studies, analyzing results, and drawing conclusions from clinical data.
Communication skills: Ability to write clearly about technical and clinical topics for diverse audiences.
Self-directed: Comfortable with ambiguity and able to identify and solve high-impact problems independently.

Nice To Haves

Board certification or fellowship in Clinical Informatics (AMIA, ABPM).
Experience with ambient clinical documentation, clinical NLP, or AI scribe products.
Track record of building clinical AI prototypes, tools, or evaluation frameworks.
Publication record in clinical informatics, biomedical NLP, health AI, or related fields.
Experience with ML frameworks (PyTorch, HuggingFace, LangChain) or cloud ML platforms.
Background in clinical decision support system design or clinical quality improvement.
Product management, UX research, or technical program management experience in health tech.
Product sense: ability to see the bigger picture of what should be built and why.

Responsibilities

Aid in executing clinical evaluation studies, including building benchmarks, defining ground truth, and measuring model accuracy.
Curate and structure clinical datasets for model training, fine-tuning, and evaluation.
Develop and iterate on prompt architectures for clinical AI agents, covering documentation, coding, clinical decision support, and diagnostic reasoning.
Analyze model failure modes with scientific rigor, identifying systematic errors, quantifying clinical risk, and designing mitigations.
Prototype new clinical AI capabilities in collaboration with ML engineers.
Build evaluation pipelines and quality dashboards to track clinical accuracy metrics.
Conduct statistical analysis on clinical outcomes data.
Develop clinical safety guardrails and automated quality checks for AI-generated outputs.
Own clinical terminology systems and interoperability mappings.
Serve as the clinical subject matter expert within product and engineering teams.
Design and lead clinical validation studies with practicing physicians.
Stay current on clinical AI literature and publish findings or present at conferences.
Collaborate with ML/NLP engineers on model architecture decisions.