At Swoop, our mission is simple: Improving patient outcomes. As a market leader in AI-driven, data-powered, and privacy-compliant healthcare engagement, we connect the signals that drive earlier diagnosis, better treatment decisions, and sustained adherence.
Our integrated, privacy-safe platform, enables life sciences companies to drive measurable patient and commercial outcomes with actionable intelligence — spanning patient and HCP audience targeting, community engagement, AI-powered web solutions, coordinated omnichannel activation, and prescription fulfillment.
We believe our people are our greatest asset. 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 and 2026. We are driven by a patient-first philosophy and passionate about leveraging technology to create a healthier future.
After becoming an independent company in 2024, Swoop experienced significant growth and demonstrated an unwavering commitment to innovation, talent development, and enhancing the patient experience. Our acquisition of MyHealthTeam in January 2025 and Nimble in May 2026 helped us further bridging the gap between healthcare brands and patients for more impactful and targeted engagement. Empowering millions of patients nationwide to connect, share resources, and access medically verified information to better manage their health. MyHealthTeam hosts the largest and most engaged patient social networks in healthcare, offering more than 70 condition-specific communities for chronic and rare conditions. As a prescription fulfillment and patient engagement platform, Nimble empowers independent and regional pharmacies to deliver a modern, consumer-friendly healthcare experience while streamlining pharmacy operations.
If you're a driven professional seeking to make a real difference in healthcare marketing at a fast-growing, innovative company, join Swoop and help us revolutionize how brands connect with patients and HCPs.
Join us to redefine the patient experience
MyHealthTeam builds communities for people living with chronic and rare conditions. We reach millions of people each month, and we’re investing deeply in AI to help members find the right support, content, and recommendations — safely, responsibly, and at scale.
We’re looking for a Staff AI/ML Engineer who is product-minded, hands-on, and excited to ship production ML/LLM systems that turn messy, real-world text and behavioral data into reliable experiences for patients.
What you get to do every day
- 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.
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)
- Experience designing LLM safety systems: red-teaming, adversarial testing, prompt injection mitigation, output filtering, human-in-the-loop review
Some tools we use
- Databricks/Spark for distributed processing
- 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.
Why MyHealthTeam @ Swoop
- 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
Application materials
Submit a resume and answer the following two short questions:
- Tell us why MyHealthTeam’s mission matters to you.
- Give one example of a production ML/LLM system you shipped and how you evaluated it.
