Research Engineer
Lotus AI, San Francisco
What you will do
- Research and Modeling
- Build and iterate on agentic workflows that use retrieval, function calling, and planning to answer health questions with citations and uncertainty awareness
- Fine tune and distill models for summarization, extraction, classification, and dialogue grounded in medical data
- Develop abstention, routing, and fallback strategies that favor safety and correctness
- Datasets and Labeling
- Curate high quality datasets from EHR, claims, labs, devices, and chat logs with rigorous deidentification
- Design synthetic data and clinician-in-the-loop labeling pipelines that reflect real clinical use
- Evaluation and Safety
- Own the eval stack for medical correctness, hallucination, bias, safety, latency, and cost
- Build automated red teaming and regression suites tied to clinical guidelines and source citations
- Systems and Production
- Ship research to production with robust observability, feature flags, and rollback plans
- Optimize inference with batching, quantization, LoRA or QLoRA, and vLLM or TensorRT where appropriate