[Remote] Staff AI/ML Engineer, MyHealthTeam

Note: The job is a remote job and is open to candidates in USA. Swoop is a market leader in privacy-safe healthcare marketing, and they are seeking a Staff AI/ML Engineer to build and deploy machine learning systems that enhance patient experiences. The role involves developing LLM applications, maintaining data pipelines, and collaborating with various teams to ensure the quality and safety of AI outputs.

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
• (Staff scope) Lead architecture and technical direction for applied AI across the organization; mentor engineers; establish best practices and reusable platforms

Skills

• 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
• 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)
• Experience designing LLM safety systems: red-teaming, adversarial testing, prompt injection mitigation, output filtering, human-in-the-loop review

Benefits

• 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.
• Mission-driven work with massive reach: help millions of people find support and better health outcomes
• High-ownership culture: small teams, fast shipping, visible impact
• Strong collaboration: product, data, and domain experts working together
• A chance to shape applied AI in a real consumer product with real constraints
• Swoop fosters a culture of innovation and continuous learning, providing employees with rich opportunities for professional growth.
• This commitment to our team earned us the "Best Places to Work" recognition from Business Intelligence Group in 2025, based on a survey of over 100 employees.

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