{"id":787059,"url":"https://alion.io/job/lotushealth-member-of-technical-staff","title":"Member of Technical Staff","company":{"id":720329,"name":"Lotus","domain":"lotus.ai","url":"https://alion.io/company/lotus-3","size_band":"201-500","is_staffing_agency":false,"is_intermediary":false,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-23T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":197000,"max_usd":402000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":503},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RLHF","optional":false},{"name":"Text-to-Speech","optional":false},{"name":"Tool Use","optional":false},{"name":"Voice Agents","optional":false},{"name":"Amazon ECS","optional":true},{"name":"Axolotl","optional":true},{"name":"ClickHouse","optional":true},{"name":"Computer Vision","optional":true},{"name":"Docker","optional":true},{"name":"DPO","optional":true},{"name":"DuckDB","optional":true},{"name":"FastAPI","optional":true},{"name":"Hugging Face","optional":true},{"name":"Langfuse","optional":true},{"name":"PyTorch","optional":true},{"name":"RAG","optional":true},{"name":"Sentry","optional":true},{"name":"SQLAlchemy","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-02-20T02:23:53Z","employer_posted_date":"2026-02-20","last_verified_at":"2026-09-23T21:48:42Z","board_verified":true,"closed_at":null,"days_open":216,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":215},"description":"Member of Technical Staff @ Lotus AI\nWho we are\nLotus AI is a groundbreaking primary care app that integrates your medical records, AI, and real doctors to provide free, personalized healthcare and prescriptions.\nOur 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.\nWhat this role is\nYou'll help build and operate the AI + data systems behind AI-driven primary care.\n\nThis is a generalist role. You may work across model training and fine-tuning, model tooling, data pipelines, retrieval/evals, and product workflows.\n\nYou'll be close to the core system and involved in product decisions from day 1.\n\nYou'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.\n\nYou'll help shape our real-time voice and video AI capabilities, building the foundation for intelligent, multimodal patient interactions.\n\nWhat this role is not\nNot a big-company role with tight scope and clear lanes.\n\nNot a place with a formal hierarchy or long onboarding ramp.\n\nNot a “ticket queue” job. Priorities will change week to week based on user needs, clinician feedback, safety issues, and what’s breaking.\n\nWhat you’ll do\nAI Agents and Product Intelligence\nBuild and iterate on AI agent workflows that handle multi-step clinical reasoning, tool use, and structured decision-making.\n\nDesign guardrails, fallback logic, and escalation paths to ensure safe autonomous behavior in patient-facing products.\n\nPrototype and ship new AI-powered product features end-to-end, from model selection to UX integration.\n\nAI Knowledge Base and Search Improvements\nImprove knowledge bases so that citations resolve to original data and searches are fast, relevant, and prioritize tier-one medical information.\n\nContinuously enhance retrieval accuracy and data lineage tracking.\n\nAI Data Ingestion and Integrity\nRebuild data pipelines to eliminate stale data, support clinician and patient corrections, and ensure full traceability.\n\nDesign models that sync cleanly with health data partners and credentialing authorities.\n\nBuild and maintain data curation pipelines that produce high-quality training and evaluation datasets from clinical interactions.\n\nVoice and Video AI\nBuild and optimize real-time voice pipelines for patient-facing interactions, including speech-to-text, natural language understanding, and text-to-speech.\n\nDevelop low-latency, streaming voice agents that can conduct clinical intake, triage, and follow-up conversations with empathy and medical accuracy.\n\nFine-tune voice and video models for medical terminology, diverse accents, and accessibility needs.\n\nDesign interruption handling, turn-taking logic, and conversational state management for natural, fluid voice experiences.\n\nObservability and Analytics\nBuild monitoring and analytics for background jobs to monitor failure rates and identify partner vs. internal issues.\n\nStreamline tracing, logging, and auditing to reduce redundancy while maintaining compliance-grade visibility.\n\nInstrument model performance tracking in production - monitoring latency, token usage, output quality, and drift over time.\n\nModel Training and Fine-Tuning\nFine-tune and adapt foundation models on clinical data to improve diagnostic accuracy, safety, and tone for patient-facing interactions.\n\nDesign and run training pipelines including data curation, annotation workflows, hyperparameter tuning, and model evaluation.\n\nDevelop and maintain evaluation frameworks (automated and human-in-the-loop) to measure model quality, safety, and regression across releases.\n\nExperiment with prompt engineering, RLHF, distillation, and other techniques to optimize model behavior for healthcare-specific use cases.\n\nWhat you bring\nStrong programming skills, preferably Python\n\nExperience with system refactors, schema migrations, and data infrastructure simplification\n\nFamiliarity with PostgreSQL (including JSONB and vector types) and AWS\n\nExperience building production systems that power AI or ML workflows\n\nHands-on experience with LLM APIs, prompt engineering, and shipping AI-powered product features\n\nComfort working across the stack, from schema design to production debugging\n\nBonus points\nFamiliarity with training infrastructure and frameworks (PyTorch, Hugging Face, vLLM, Axolotl, or similar)\n\nExperience with RLHF, DPO, or other alignment and preference-tuning techniques\n\nExperience building or improving AI agent systems with tool use and multi-step reasoning\n\nExperience building retrieval systems for LLMs (RAG pipelines, vector search, grounding)\n\nFamiliarity with FastAPI, SQLAlchemy, DuckDB, Temporal, ClickHouse, Valkey, or similar systems\n\nExperience with real-time voice AI systems, speech models, computer vision, medical imaging, or multimodal models that combine text, audio, and visual inputs\n\nKnowledge of logging/monitoring stacks (Sentry, Langfuse) and containerized deployments (Docker, ECS)\n\nExperience simplifying multi-layered data systems where architectural issues cascade through storage, logging, and application layers\n\nStrong intuition for designing systems that balance correctness, observability, and performance\n\nWhy Lotus\nWe 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.\nAs 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.\nWhat success will look like in the first 90 days\n30 days\nShipping reliably, understands the core system, owns a small surface area\n\n60 days\nOwning a meaningful system and improves a key metric (quality, latency, clinician wait time, data reliability, etc.)\n\n90 days\nIndependently driving a roadmap slice and raises the team’s bar (agents/evals/monitoring)\n\nWhat the interview process looks like\nQuick intro call\n\nShort technical screen\n\n1 deeper technical interview + team chat\n\nReferences + offer\n\nWe usually wrap the process in ~7-10 days","description_format":"text","description_chars":6375,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Biotechnology","Health Care","Search Engines"],"lifecycle":[{"event":"open","at":"2026-09-12T02:14:29Z"}],"liveness":{"score":10,"band":"cold","label":"Long shot","p_open":1,"p_active":0.343,"p_room":0.28,"age_days":215,"expected_fill_days":55,"reasons":["conf:1","win:tail","crowd:"],"computed_at":"2026-09-23T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/lotushealth-member-of-technical-staff","json_url":"https://alion.io/job/lotushealth-member-of-technical-staff.json","meta":{"generated_at":"2026-09-24T03:35:29Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}