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Conduct research advancing healthcare AI, including model evaluation, clinical benchmarks, human assessments, health-focused model development, agentic systems, prototyping, and simulations. The role involves pre-training and post-training workflows, research publications, collaboration with clinical and engineering teams, and sharing research practices. Candidates will apply expertise in machine learning, natural language processing, clinical data, and healthcare data standards such as FHIR and OMOP.

Lead frontend design and implementation for patient and physician products, shape frontend architecture (design systems, state, testing, performance), integrate AI/LLM features with backend/ML teams, and deliver polished, accessible, high-performance interfaces in a hybrid NYC/Boston environment.

Lead full‑stack engineering initiatives to build AI-enabled healthcare products. Ship physician-facing web and mobile features, integrate ML/LLM tooling, architect scalable back-end systems and health data pipelines, and collaborate cross-functionally with product, clinical, and research teams.

Full‑stack engineer shipping AI-enabled clinical products. Build physician-facing web and mobile interfaces, integrate ML/LLM features to production, and develop enterprise integrations (EMR, claims, HIE). Collaborate with product, design, clinical, and ML teams to scale infrastructure for hundreds of thousands of users.

Own and evolve core backend platform primitives: external APIs, retrieval/search (RAG) infrastructure, unified health data models including time-series, and enterprise integrations to support AI-driven clinical workflows and multi-tenant reliability.

The Backend Infrastructure Engineer develops and maintains infrastructure, manages CI/CD pipelines, writes backend services, enhances developer experience, and ensures security compliance in a hybrid role.