Curie
AI Engineer
Chicago
Sponsorship not specifiedDetected 124 days ago
PythonGoPostgreSQLVector DatabasesAWSGCPAzureCloud PlatformsgRPCMachine LearningTensorFlowPyTorchData EngineeringNLPLLMsRAGAgentic AILangGraphAI OrchestrationComplianceDesign SystemsHIPAAHL7/FHIREHR/EMR
About the role
- Improve retrieval accuracy, citation traceability, and relevance ranking to ensure AI-surfaced information is trustworthy and explainable.
- Continuously evaluate and iterate on retrieval quality with structured benchmarks.
- Instrument AI workflows with tracing, logging, and evaluation hooks for compliance-grade visibility into model behavior.
Responsibilities
- Own session and memory management for long-running clinical agents - ensuring continuity, safety, and auditability across patient interactions.
- Extend and maintain the Python service that communicates with other microservices, handling structured clinical data and LLM integrations.
- Build validation layers and guardrails that ensure clinical outputs meet safety thresholds before reaching patients or providers.
- You'll own the Python and Go service layer that powers our clinical AI processing - from multi-step intake reasoning to retrieval-augmented generation for treatment guidance.
- Design systems that respect HIPAA requirements end-to-end - from data handling to model I/O to audit logging.
Requirements
- Strong Python expertise - you're comfortable with async services, and modern tooling (uv, Pyright, etc.).
- Familiarity with PyTorch, TensorFlow, or Hugging Face Transformers for custom model work.
- Hands-on experience with LLMs in production: prompt engineering, structured output, evaluation, and iteration - across commercial APIs (OpenAI, Anthropic, Google) or open-source models (LLaMA, Gemma, etc.).
- Familiarity with agentic AI patterns - multi-step reasoning, tool use, and orchestration frameworks (LangGraph, Google ADK, CrewAI, Claude Agent SDK, or equivalent).
- Comfort working across service boundaries - you can navigate a Go backend, gRPC interfaces, and cloud infrastructure when needed.
- Experience with cloud ML platforms: GCP/Vertex AI, AWS SageMaker, or Azure ML.
- Familiarity with model evaluation frameworks (RAGAS, DeepEval, custom evals) and LLM observability tools (Langfuse, LangSmith, Arize, Weights & Biases).
- Experience with PostgreSQL (including JSONB, pgvector), sqlc, or gRPC/Connect-RPC.
- 5+ years of software engineering experience, with meaningful time building production AI/ML systems.
- Experience building RAG pipelines, vector search, or retrieval systems for grounding LLM outputs - using tools like LangChain, LlamaIndex, or custom implementations.
- Strong intuition for system design that balances correctness, observability, and performance.
- Curiosity about healthcare and a desire to build AI that's safe, explainable, and clinically useful.
- Hands-on with local/self-hosted LLM inference: vLLM, Ollama, TGI, or GGUF-based deployments.
- Fine-tuning or distillation experience - LoRA, QLoRA, RLHF, DPO, or similar techniques.
Skills
- Direct impact on making quality healthcare more accessible.
Compensation
- Competitive salary, significant equity, and benefits in a well-funded company with aggressive growth targets.
Benefits
- Familiarity with healthcare data standards (FHIR, HL7) or EHR integrations.
- Background in medical AI safety, bias detection, or clinical validation.
- Published work or deep domain knowledge in healthcare AI or clinical NLP.
- Shape the AI architecture of a healthcare product from day one - your decisions will directly impact patient care at scale.
- Design and implement multi-step AI agent pipelines that process patient intake, synthesize medical history, and surface clinical recommendations.
- Build and optimize RAG pipelines that ground clinical AI outputs in authoritative medical guidelines, drug references, and treatment protocols - including embedding models, vector stores, and reranking strategies.
Company info
- We're looking for an AI Engineer to design and build the AI systems at the center of Curie's clinical platform.
This listing is sourced directly from Curie's careers page and normalized into a canonical job model.