Hybrid Staff AI/ML Engineer

at Swoop

• Job title: Staff AI/ML Engineer
• Job type: Full Time
• Experience level: Lead
• Salary: $240,000 - $260,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Python, Spark
• Location requirements: Hybrid, San Francisco, United States

About the role

• Staff AI/ML Engineer developing production ML/LLM systems for enhancing health experiences at MyHealthTeam. Leading technology direction, mentoring, and establishing best practices in a hybrid work environment.

Responsibilities

• 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