Staff AI/ML Engineer, MyHealthTeam

What you get to do every day Develop LLM applications with retrieval and tool use (e.g., RAG, orchestration/workflows, structured extraction) to deliver trustworthy consumer health experiences. Convert unstructured text (posts, comments, messages, search queries) into structured signals (topics, entities, intent, sentiment, safety flags) using a mix of classical NLP and modern LLMs. Create and maintain data pipelines for training, inference, evaluation, and analytics (batch and/or streaming as needed). Design evaluation systems that measure quality and safety: offline metrics, golden datasets, human review workflows, and online A/B testing alignment. Implement production guardrails to reduce harm and misinformation risk (policy constraints, refusal behavior, citations/attribution when appropriate, red‑team‑ing, monitoring, and incident response). Set up monitoring for model + system health (latency, cost, drift, regressions, quality metrics). Partner closely with the Product, Engineering, and Data teams and clinical/subject‑matter experts to validate outputs and define what “correct” means for sensitive, health‑adjacent use cases. (Staff scope) Lead architecture and technical direction for applied AI across the organization; mentor engineers; establish best practices and reusable platforms. Examples of problems you might work on Personalized recommendations for communities, posts, resources, or next‑best actions Safer content understanding: detection of misleading/high‑risk health claims, escalation workflows Search and discovery improvements using embeddings, hybrid retrieval, and ranking Summarization and structuring of long threads into navigable insights (with safety constraints) Member intent understanding from behavioral + text signals Must‑have qualifications 8+ years building and shipping production ML systems (or equivalent experience with demonstrable impact) Strong Python skills and experience with ML/LLM libraries and tooling (e.g., Hugging Face ecosystem, LangChain/LangGraph, or equivalent) Proven ability to design production‑grade pipelines (training/inference/eval) and operate models in real systems (monitoring, rollbacks, incident handling) Solid grounding in ML fundamentals (NLP, deep learning, statistical reasoning, evaluation) Experience with MLOps best practices: versioning, reproducibility, CI/CD, model registry patterns, feature/data management, and infrastructure collaboration Experience working with large‑scale data using Databricks/Spark or equivalent distributed processing Strong product and stakeholder instincts: you can translate ambiguous business needs into measurable ML outcomes Nice‑to‑have qualifications Experience building RAG and retrieval systems: vector databases, hybrid search, ranking, recommendation, query understanding Experience in healthcare or regulated environments, including privacy‑by‑design, auditability, and safety reviews (HIPAA/PHI familiarity a plus) Experience with streaming/clickstream data, experimentation platforms, and causal/measurement thinking Ability to prototype end‑to‑end experiences (e.g., Streamlit, Gradio, lightweight frontends) Some tools we use Redshift and BI tools (Looker/Tableau) for analytics Terraform for infrastructure‑as‑code; Airflow for orchestration; GitHub Actions for CI/CD AWS (including Bedrock) and a mix of private and open‑weight models (including fine‑tunes where appropriate) Experimentation tooling (A/B testing) and internal UX analytics tools AI‑assisted coding tools (e.g., Cursor, Copilot, Claude/OpenAI tooling) Working model The MyHealthTeam Engineering team operates in a remote‑first environment. This role is fully remote, with optional in‑person collaboration at our San Francisco office. The pay range for this role is: 240,000 - 260,000 USD per year (Remote (United States)) #J-18808-Ljbffr MyHealthTeam