Curie - AI Engineer

• 5+ years of software engineering experience, with meaningful time building production AI/ML systems. • 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.). • Experience building RAG pipelines, vector search, or retrieval systems for grounding LLM outputs — using tools like LangChain, LlamaIndex, or custom implementations. • 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. • 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. • Experience with cloud ML platforms: GCP/Vertex AI, AWS SageMaker, or Azure ML. • 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. • Familiarity with model evaluation frameworks (RAGAS, DeepEval, custom evals) and LLM observability tools (Langfuse, LangSmith, Arize, Weights & Biases). • Familiarity with healthcare data standards (FHIR, HL7) or EHR integrations. • Background in medical AI safety, bias detection, or clinical validation. • Experience with PostgreSQL (including JSONB, pgvector), sqlc, or gRPC/Connect-RPC. • Startup experience — especially as a founder, founding engineer, or early employee. • Published work or deep domain knowledge in healthcare AI or clinical NLP.