Role Description We're looking for a Senior AI Engineer to design and build the AI systems at the center of Curie's clinical platform. 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. This is an opportunity to shape the AI architecture of a healthcare product from the ground up, working closely with founders, clinicians, and engineers across the stack. What You'll Build - Agentic Clinical Workflows - Design and implement multi-step AI agent pipelines that process patient intake, synthesize medical history, and surface clinical recommendations. - Build orchestration patterns for managed, observable AI/ML workflows on cloud infrastructure. - Own session and memory management for long-running clinical agents — ensuring continuity, safety, and auditability across patient interactions. - Retrieval & Medical Knowledge Systems - 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. - 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. - Clinical Data Infrastructure - Extend and maintain the Python service that communicates with other microservices, handling structured clinical data and LLM integrations. - Build data pipelines for ingesting, normalizing, and reconciling health data from external partners and EHR integrations. - Design systems that respect HIPAA requirements end-to-end — from data handling to model I/O to audit logging. - Observability & Safety - Instrument AI workflows with tracing, logging, and evaluation hooks for compliance-grade visibility into model behavior. - Build validation layers and guardrails that ensure clinical outputs meet safety thresholds before reaching patients or providers. - Monitor failure rates, latency, and model drift across production AI systems. What You Bring - 7+ 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. Bonus Points - 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. Why Curie - Shape the AI architecture of a healthcare product from day one — your decisions will directly impact patient care at scale. - Work alongside a world-class team of engineers, clinicians, and ex-founders who've built and scaled products before. - Competitive salary, significant equity, and benefits in a well-funded company with aggressive growth targets. - Build with modern infrastructure: Vertex AI, SageMaker, AI frameworks, Go + Python — no legacy baggage. - Direct impact on making quality healthcare more accessible.

Role Description We're looking for an AI Engineer to design and build the AI systems at the center of Curie's clinical platform. 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. This is an opportunity to shape the AI architecture of a healthcare product from the ground up, working closely with founders, clinicians, and engineers across the stack. What You'll Build - Agentic Clinical Workflows - Design and implement multi-step AI agent pipelines that process patient intake, synthesize medical history, and surface clinical recommendations. - Build orchestration patterns for managed, observable AI/ML workflows on cloud infrastructure. - Own session and memory management for long-running clinical agents — ensuring continuity, safety, and auditability across patient interactions. - Retrieval & Medical Knowledge Systems - 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. - 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. - Clinical Data Infrastructure - Extend and maintain the Python service that communicates with other microservices, handling structured clinical data and LLM integrations. - Build data pipelines for ingesting, normalizing, and reconciling health data from external partners and EHR integrations. - Design systems that respect HIPAA requirements end-to-end — from data handling to model I/O to audit logging. - Observability & Safety - Instrument AI workflows with tracing, logging, and evaluation hooks for compliance-grade visibility into model behavior. - Build validation layers and guardrails that ensure clinical outputs meet safety thresholds before reaching patients or providers. - Monitor failure rates, latency, and model drift across production AI systems. Qualifications - 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. Bonus Points - 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. Benefits - Shape the AI architecture of a healthcare product from day one — your decisions will directly impact patient care at scale. - Work alongside a world-class team of engineers, clinicians, and ex-founders who've built and scaled products before. - Competitive salary, significant equity, and benefits in a well-funded company with aggressive growth targets. - Build with modern infrastructure: Vertex AI, SageMaker, AI frameworks, Go + Python — no legacy baggage. - Direct impact on making quality healthcare more accessible.

• Design and build our test automation strategy and framework from scratch, utilizing Cypress, and lead the implementation of AI-driven testing tools (e.g., visual regression or self-healing tests) to optimize our quality assurance process. • Develop, execute, and maintain automated tests for our APIs, UIs, and backend services to ensure the quality of our platform. • Integrate automated tests seamlessly into our CI/CD pipelines using GitHub Actions to enable continuous testing and delivery. • Establish and evangelize QA automation best practices, patterns, and standards across the engineering organization. • Collaborate closely with platform developers working with Java, Python, and Golang to ensure new features are designed with testability in mind. • Perform targeted exploratory and non-functional testing (e.g., performance, load) to identify critical bugs and performance bottlenecks. • Act as the primary quality advocate within the Platform team and contribute to the QA community across the company.

• Create testing-related documentation, including test plans, test cases/scripts, and bug reports assessing quality and associated risk • Automate features for better regression coverage • Triage and/or assist with triaging of automation results • Develop deep expertise in the product • Conduct QA tests and verify outcomes within schedules/timelines • Work with software engineers, product managers, and other quality engineers in an Agile team environment • Operate at architecture and code level, driving technical discussions during design/implementation reviews • Be the technical quality expert in functional areas and influencing decisions that will help build quality into the product • Be comfortable providing technical leadership to junior teammates, enabling them to achieve targeted goals • Conduct POCs and make recommendations that would help raise the quality bar • Enhance your knowledge of code coverage tools and metrics • Work with quality management to come up with new processes and roll them out across the organization • Become a technical contributor, a product expert, and a team project manager and support your QA manager as and when you work on the product