Build end-to-end ML/LLM features from problem definition → data → modeling → evaluation → deployment → monitoring.
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-teaming, 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.
Lead architecture and technical direction for applied AI across the organization; mentor engineers; establish best practices and reusable platforms.

Requirements

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.

Benefits

Health insurance
401(k) matching
Flexible work hours
Paid time off
Professional development opportunities

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learninglarge language modelsnatural language processingdata pipelinesmodel evaluationproduction ML systemsPythonMLOpsdeep learningstatistical reasoning

Soft Skills

leadershipmentoringstakeholder managementproblem-solvingcommunication