Member of Technical Staff

If you’re excited to rebuild healthcare, tell us why we should work together.

Lotus 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.

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.

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.

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.

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.

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.

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.

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.

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…