Member of Technical Staff

Job description

# Member of Technical Staff @ Lotus AI ## Who we are [Lotus AI](https://lotushealth.ai/) is a groundbreaking primary care app that integrates your medical records, AI, and real doctors to provide free, personalized healthcare and prescriptions. Our team includes ex-founders and engineers who have built and scaled consumer apps to millions of users with prior successful exits. Lotus is backed by Kleiner Perkins, CRV, clinicians at Harvard and Stanford among others. ## What this role is - You'll help build and operate the AI + data systems behind AI-driven primary care. - This is a generalist role. You may work across model training and fine-tuning, model tooling, data pipelines, retrieval/evals, and product workflows. - You'll be close to the core system and involved in product decisions from day 1. - You'll design and scale the data and retrieval systems that power Lotus's clinical AI, improving correctness, traceability, and explainability in how medical information is surfaced, validated, and applied in real-world care. - You'll help shape our real-time voice and video AI capabilities, building the foundation for intelligent, multimodal patient interactions. ## What this role is not - Not a big-company role with tight scope and clear lanes. - Not a place with a formal hierarchy or long onboarding ramp. - Not a “ticket queue” job. Priorities will change week to week based on user needs, clinician feedback, safety issues, and what’s breaking. ## What you’ll do - AI Agents and Product Intelligence - Build and iterate on AI agent workflows that handle multi-step clinical reasoning, tool use, and structured decision-making. - Design guardrails, fallback logic, and escalation paths to ensure safe autonomous behavior in patient-facing products. - Prototype and ship new AI-powered product features end-to-end, from model selection to UX integration. - AI Knowledge Base and Search Improvements - Improve knowledge bases so that citations resolve to original data and searches are fast, relevant, and prioritize tier-one medical information. - Continuously enhance retrieval accuracy and data lineage tracking. - AI Data Ingestion and Integrity - Rebuild data pipelines to eliminate stale data, support clinician and patient corrections, and ensure full traceability. - Design models that sync cleanly with health data partners and credentialing authorities. - Build and maintain data curation pipelines that produce high-quality training and evaluation datasets from clinical interactions. - Voice and Video AI - Build and optimize real-time voice pipelines for patient-facing interactions, including speech-to-text, natural language understanding, and text-to-speech. - Develop low-latency, streaming voice agents that can conduct clinical intake, triage, and follow-up conversations with empathy and medical accuracy. - Fine-tune voice and video models for medical terminology, diverse accents, and accessibility needs. - Design interruption handling, turn-taking logic, and conversational state management for natural, fluid voice experiences. - Observability and Analytics - Build monitoring and analytics for background jobs to monitor failure rates and identify partner vs. internal issues. - Streamline tracing, logging, and auditing to reduce redundancy while maintaining compliance-grade visibility. - Instrument model performance tracking in production — monitoring latency, token usage, output quality, and drift over time. - Model Training and Fine-Tuning - Fine-tune and adapt foundation models on clinical data to improve diagnostic accuracy, safety, and tone for patient-facing interactions. - Design and run training pipelines including data curation, annotation workflows, hyperparameter tuning, and model evaluation. - Develop and maintain evaluation frameworks (automated and human-in-the-loop) to measure model quality, safety, and regression across releases. - Experiment with prompt engineering, RLHF, distillation, and other techniques to optimize model behavior for healthcare-specific use cases. ## What you bring - Strong programming skills, preferably Python - Experience with system refactors, schema migrations, and data infrastructure simplification - Familiarity with PostgreSQL (including JSONB and vector types) and AWS - Experience building production systems that power AI or ML workflows - Hands-on experience with LLM APIs, prompt engineering, and shipping AI-powered product features - Comfort working across the stack, from schema design to production debugging ## Bonus points - Familiarity with training infrastructure and frameworks (PyTorch, Hugging Face, vLLM, Axolotl, or similar) - Experience with RLHF, DPO, or other alignment and preference-tuning techniques - Experience building or improving AI agent systems with tool use and multi-step reasoning - Experience building retrieval systems for LLMs (RAG pipelines, vector search, grounding) - Familiarity with FastAPI, SQLAlchemy, DuckDB, Temporal, ClickHouse, Valkey, or similar systems - Experience with real-time voice AI systems, speech models, computer vision, medical imaging, or multimodal models that combine text, audio, and visual inputs - Knowledge of logging/monitoring stacks (Sentry, Langfuse) and containerized deployments (Docker, ECS) - Experience simplifying multi-layered data systems where architectural issues cascade through storage, logging, and application layers - Strong intuition for designing systems that balance correctness, observability, and performance ## Why Lotus We are redefining how healthcare data is understood and acted upon. You’ll work with a world-class group of engineers, clinicians, and AI researchers to build something with lasting impact to improve healthcare. As an AI Engineer on a small, exceptional team you’ll have the autonomy to build the systems that make our clinical AI safe, fast, and explainable. Your work will directly influence patient care at scale. ## What success will look like in the first 90 days 30 days - Shipping reliably, understands the core system, owns a small surface area 60 days - Owning a meaningful system and improves a key metric (quality, latency, clinician wait time, data reliability, etc.) 90 days - Independently driving a roadmap slice and raises the team’s bar (agents/evals/monitoring) ## **What the interview process looks like** - Quick intro call - Short technical screen - 1 deeper technical interview + team chat - References + offer - We usually wrap the process in ~7–10 days