About Suki At Suki, we’re designing healthcare technology that solves one of the biggest problems in medicine: clinician burnout. Our proprietary Ambient Clinical Intelligence (ACI) platform is built in partnership with clinicians, with the mission of meaningfully improving their lives, their patients’ lives, and the healthcare system as a whole. As the only purpose-built AI platform to span the entire clinical workflow, Suki frees clinicians from heavy administrative burdens and brings them back to the reason they chose medicine in the first place: caring for patients. Our flagship product, Suki for Clinicians, plugs into the pre-visit, patient encounter, and post-visit stages, automating documentation, reducing note-taking time by 41%, and allowing clinicians to focus on the person in front of them rather than on paperwork. Our ACI doesn’t just listen in the background; it functions as an invisible layer that understands, contextualizes, and helps with everything from pre-charting to revenue cycle management. On average, clinicians who use Suki see a 60% reduction in feelings of burnout, an 81% increase in practice satisfaction, and a $1,688 increase in incremental monthly revenue per user. Our partner product, Suki for Partners, allows EHRs, telehealth platforms, RCM solutions, care management systems, and hardware partners to leverage APIs and SDKs through the Suki Developer Layer in order to deliver transformative ACI inside their own surfaces. The Role We are looking for a physician-scientist who builds. Reporting to the Chief Medical Officer, you will work at the intersection of clinical medicine, product, data science, and AI engineering — directly shaping the models and systems that power Suki’s clinical intelligence. This is a deeply technical, hands-on role: you will design evaluation frameworks, curate clinical datasets for clinical reasoning, write and refine prompts, analyze model outputs, and build prototypes. You are as comfortable in a Jupyter notebook as you are in an exam room. This is not a product management role. We want someone whose first instinct is to look at the data, run the experiment, and build the thing — not write a PRD about it. That said, candidates who can also think at the product and systems level will thrive here, because you’ll have significant influence over what we build and why. What You’ll Do Clinical AI Research & Development - Aide in executing clinical evaluation studies — build benchmarks, define ground truth, measure model accuracy across specialties and clinical scenarios - Curate and structure clinical datasets for model training, fine-tuning, and evaluation (encounter transcripts, clinical notes, terminology mappings, diagnostic patterns) - Develop and iterate on prompt architectures for clinical AI agents — documentation, coding (ICD-10/E&M), clinical decision support, and diagnostic reasoning - Analyze model failure modes with scientific rigor: identify systematic errors, quantify clinical risk, and design mitigations - Prototype new clinical AI capabilities (clinical reasoning/CDS alerts, differential diagnosis support, order suggestions) in collaboration with ML engineers Clinical Data Science - Build evaluation pipelines and quality dashboards that track clinical accuracy metrics across encounters, specialties, and EHR systems - Conduct statistical analysis on clinical outcomes data — documentation accuracy, coding precision/recall, time savings, clinician satisfaction - Develop clinical safety guardrails and automated quality checks for AI-generated outputs (drug interactions, contraindications, terminology accuracy) - Own clinical terminology systems and interoperability mappings (SNOMED-CT, ICD-10, LOINC, RxNorm, HL7 FHIR) that underpin Suki’s clinical intelligence Clinical Expertise & Collaboration - Serve as the clinical subject matter expert embedded within product and engineering — review model outputs, validate clinical reasoning, and flag edge cases that require domain knowledge - Design and lead clinical validation studies with practicing physicians, translating qualitative feedback into quantitative improvement targets - Stay current on clinical AI literature, publish findings or present at clinical informatics conferences when appropriate - Collaborate directly with ML/NLP engineers on model architecture decisions where clinical domain knowledge is critical Requirements - Clinical background: MD or DO with 3+ years of direct patient care experience strongly preferred. Candidates with other advanced clinical or doctoral degrees (PharmD, PsyD, PhD, DNP, RN with graduate-level training) and demonstrable expertise in clinical AI, informatics, or computational medicine will be considered for specialized team initiatives and projects on a case-by-case basis - Technical proficiency: Comfortable working in Python, SQL, or R for data analysis. You regularly use Jupyter notebooks, pandas, or similar tools to explore data and build analyses - AI/ML literacy: Working understanding of large language models, prompt engineering, and clinical NLP. You don’t need to train models from scratch, but you should understand how they work well enough to debug outputs and guide engineering decisions - Deep familiarity with at least one major EHR system (Epic, Oracle Health/Cerner, MEDITECH, athenahealth) and working knowledge of clinical interoperability standards (HL7 FHIR, SNOMED-CT, ICD-10, LOINC) - Scientific rigor: Experience designing studies, analyzing results, and drawing defensible conclusions from clinical data. You think in terms of hypotheses, metrics, and experiments - Communication: Ability to write clearly about technical and clinical topics for both engineering and clinical audiences - Self-directed: Comfortable with ambiguity, able to identify the highest-impact problem and go solve it without waiting for a spec The Ideal Candidate You’re a physician-scientist who gravitated toward technology. Maybe you did a clinical informatics fellowship and realized you’d rather build the tools than just evaluate them. Maybe you’re an attending who taught yourself Python to analyze your own clinical data and never looked back. Maybe you’ve been in health tech for a few years and are frustrated that nobody on the engineering team truly understands clinical reasoning. You want to build things that work for real doctors, and you have the technical depth and clinical authority to do it. If you also have product sense — the ability to see the bigger picture of what should be built and why — that’s a powerful multiplier, but it’s a bonus, not a prerequisite. Nice to Have - 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 Location San Francisco Bay Area preferred. Remote candidates with willingness to travel quarterly to Redwood City, CA will be considered. Compensation In compliance with California Pay Transparency Law, the base salary range for this role is $200,000–$220,000 plus equity (ISOs). Actual compensation will depend on experience, clinical credentials, and depth of technical skills. Suki is an Equal Opportunity Employer. We are dedicated to building a company that fosters inclusion and belonging and reflects the diverse communities we serve.