Build and deploy ML models to identify missing data, predict treatment delays, triage high-risk referrals, and score payer/patient friction
Architect and maintain LLM-powered workflows
Build and maintain data pipelines to collect, clean, label, and store data across systems
Define success metrics, monitor model performance over time, and iterate quickly to improve accuracy and reliability. Set up training data, data labeling workflows or feedback loops for model improvement.
Collaborate with product, engineering, and operations to identify high-leverage automation opportunities and embed models into workflows.

6+ years in ML engineering, data science, or applied AI roles (bonus for healthcare experience).
Proficient in Python (Pandas, Scikit-learn, LangChain, PyTorch or TensorFlow) and SQL.
Strong experience building and deploying models in production environments.
Familiar with LLM integration, retrieval-augmented generation (RAG), and vector search (e.g., Pinecone, FAISS).
Skilled in working with unstructured data: OCR, NLP, form/document understanding.
Experience with cloud infrastructure (AWS preferred), Git, and basic DevOps practices.
Comfortable partnering with product and operations teams to solve real-world business problems.
Working with healthcare data (EMR/EHR, insurance claims, HL7/FHIR).
Building patient or payer-facing AI solutions in regulated environments (HIPAA).

Medical, dental, and vision insurance through our employer plan
Short and long-term disability coverage
401(k) — as an early-stage startup, and we match!
15 Days PTO — and we want you to take it!
Competitive paid parental leave and flexible return to work policy.
We invest in your career. Our company is growing quickly, and we'll give you the opportunity to do the same. You'll have access to a number of professional development opportunities so that you can keep up with the company's evolving needs and grow your career along the way.

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