Head of Applied AI

Location: New York City (Onsite, full-time)

Compensation: Base salary $250-300k + 0.5%–1.5% equity

About the Company

A venture-backed, revenue-generating healthtech startup is building an AI-native operating system for modern medicine. The mission: make personalized, data-driven private care radically more accessible and affordable across all 50 states. Small, high-output team of engineers, clinicians, and repeat founders; actively deploying AI agents in real clinical settings with measurable ROI.

The Opportunity

As the Founding AI Engineering Lead, you will architect and build the horizontal enablement layer that powers the company’s AI platform. This role blends systems architecture, applied AI, information retrieval, and data engineering—with a fast path to technical leadership and team-building.

What You’ll Do

• Architect AI data pipelines: ingestion, chunking, metadata, embeddings, and retrieval at scale.
• Build Applied AI/Agentic systems: low-latency agents, deep-research/ambient agents, and long-running event-based agents for clinical workflows.
• Develop retrieval & search: semantic + hybrid (lexical, vector, faceted, LLM-enhanced) search over unstructured medical data, relational DBs, graphs, and object stores.
• Structure messy data: extract/transform from PDFs, JSON, EHR exports into robust schemas for downstream AI.
• Own quality & performance: define metrics (retrieval accuracy, latency, data quality), optimize throughput and cost.
• Collaborate & lead: partner with backend/frontend teams; drive reviews, docs, and architectural guidance; mentor future ML/AI hires.

Tech Stack You’ll Touch

• Frontend: React, Next.js (App Router), Tailwind, TanStack
• Backend: Python (FastAPI), TypeScript (Nest.js)
• LLM & Retrieval: OpenAI, Anthropic, Gemini; AI Vercel SDK; OpenAI Agents/tool-calling; instructor; LiteLLM; SQL/pgvector; GCS
• Infra: GCP (Vertex AI, Pub/Sub), Terraform, modern CI/CD/observability

What We’re Looking For

• 6+ years in AI/ML engineering or data infrastructure.
• Deep knowledge of embeddings, RAG/hybrid retrieval, vector databases, context/memory management.
• Hands-on experience building search/retrieval pipelines (ingestion → processing → indexing).
• Strong software engineering fundamentals (modular architecture, testing, CI/CD, observability).
• Up to date on the Applied AI ecosystem (neural RAG, rerankers, multi-agent orchestration, tool calling, structured streaming outputs).
• Passion for solving high-impact, real-world data problems (healthcare/life sciences a plus).

Why This Role

• Mission-driven impact: Agents already live in clinics; your work reduces provider burnout and improves patient outcomes.
• Founding seat: Define architecture, standards, and culture from day one.
• Category creation: Help build the stack for the next era of personalized medicine.