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AI Engineer
AI Tools
Tech Stack
Agent Workflow
Building agentic intake workflows, RAG pipelines grounded in medical guidelines, session/memory management for long-running clinical agents with HIPAA-grade observability
About the Role
AI Engineer
Curie
Chicago, Illinois (Remote/Telecommute)
Salary: $150,000 - $225,000 annually + Equity
About Curie
Curie operates as a telehealth platform combining clinical expertise with AI to deliver personalized, accessible care. The team includes founders, clinicians, and engineers from Stanford, Harvard, UCLA, Berkeley, and AWS. The company is well-funded and growing aggressively.
The Role
The AI Engineer position involves designing and building AI systems for Curie's clinical platform. Responsibilities span Python and Go service layers, from patient intake reasoning through retrieval-augmented generation for treatment guidance.
Key Responsibilities
Agentic Clinical Workflows
- Design multi-step AI agent pipelines for patient intake and medical history synthesis
- Build orchestration patterns for managed, observable ML workflows on cloud infrastructure
- Manage session and memory for long-running clinical agents with safety and auditability focus
Retrieval & Medical Knowledge Systems
- Build and optimize RAG pipelines grounding outputs in medical guidelines and protocols
- Improve retrieval accuracy, citation traceability, and relevance ranking
- Evaluate retrieval quality using structured benchmarks
Clinical Data Infrastructure
- Extend Python services handling structured clinical data and LLM integrations
- Build data pipelines for ingesting and normalizing health data from EHR systems
- Design HIPAA-compliant systems for data handling, model I/O, and audit logging
Observability & Safety
- Instrument workflows with tracing, logging, and evaluation hooks for compliance visibility
- Build validation layers ensuring clinical outputs meet safety thresholds
- Monitor failure rates, latency, and model drift in production systems
Required Qualifications
- 5+ years software engineering experience with production AI/ML systems
- Strong Python proficiency with async services and modern tooling (uv, Pyright)
- Familiarity with PyTorch, TensorFlow, or Hugging Face Transformers
- Hands-on LLM production experience (OpenAI, Anthropic, Google APIs; LLaMA, Gemma)
- RAG pipeline and vector search experience (LangChain, LlamaIndex, or custom implementations)
- Knowledge of agentic AI patterns and orchestration frameworks
- Cross-service comfort navigating Go backends, gRPC interfaces, and cloud infrastructure
- Strong system design intuition balancing correctness, observability, and performance
- Healthcare curiosity and commitment to safe, explainable clinical AI
Bonus Qualifications
- Cloud ML platform experience (GCP Vertex AI, AWS SageMaker, Azure ML)
- Self-hosted LLM inference (vLLM, Ollama, TGI, GGUF)
- Fine-tuning/distillation expertise (LoRA, QLoRA, RLHF, DPO)
- Model evaluation frameworks (RAGAS, DeepEval) and observability tools (Langfuse, LangSmith)
- Healthcare data standards (FHIR, HL7) or EHR integration experience
- Medical AI safety, bias detection, or clinical validation background
- PostgreSQL expertise (JSONB, pgvector), sqlc, or gRPC/Connect-RPC
- Startup founding or early-stage engineering experience
- Published healthcare AI or clinical NLP work
Company Culture
Small, senior team with high ownership and pragmatism over perfection. AI coding tools are encouraged, and shipping iteratively is valued.