AI Engineer

Tiny Health

40d•Remote

About The Position

Tiny Health is seeking an AI Engineer to join their Product & Engineering team. This role will focus on building and scaling production-grade AI systems that power Tiny Health’s consumer and clinician-facing products. The engineer will work closely with the co-founder, product/design lead, and a small, high-performing engineering team to design and implement AI-driven features, pipelines, and infrastructure that drive meaningful health insights. The ideal candidate will have hands-on experience deploying AI applications in production, beyond prototypes or personal projects, and be comfortable owning end-to-end delivery from model integration to infrastructure. For lead-level candidates, there will be opportunities to shape the AI roadmap, set technical direction, and mentor engineers.

Requirements

Proven experience building and deploying AI or ML systems in production at scale (not just prototypes or demos).
Deep understanding of model integration workflows — from inference pipelines to prompt engineering, fine-tuning, or RAG setups.
Strong backend engineering background (Python or Node.js preferred).
Experience designing AI architectures (e.g., hybrid retrieval systems, multi-model orchestration, or embeddings).
Experience with AWS cloud infrastructure and data pipelines.
Solid grasp of API development and system architecture for scalable applications.
Comfortable working independently and driving execution in a startup environment.

Nice To Haves

Experience integrating LLMs into production products (OpenAI, Anthropic, Vertex AI, etc.).
Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma).
Experience with observability, monitoring, and evaluation for AI systems.
Experience leading projects or mentoring engineers.
Prior experience in healthtech, biotech, or working with sensitive health data.

Responsibilities

Design, build, and maintain production-grade AI systems and APIs that power our products.
Integrate and fine-tune models (LLMs, embeddings, or other ML architectures) for customer-facing and internal use cases.
Own the full lifecycle of AI features — from research and prototyping to productionization, deployment, and monitoring.
Collaborate closely with engineering, product, and science teams to scope and ship impactful AI-driven features.
Design data pipelines, retrieval systems, and scalable infrastructure for inference and model serving.
Ensure performance, observability, and reliability of AI components in production.
(Depending on experience) Lead AI initiatives, guide architecture and system design decisions, and mentor other engineers.