Principal AI Engineer - Personalization and Recommendation

Responsibilities

• Own applied AI and machine learning solutions powering patient-provider matching, clinical workflows, and patient engagement.
• Develop and fine-tune Learning-to-Rank models, embeddings, recommendation algorithms, retrieval models, and large language model integrations.
• Build production-grade search, ranking, relevance, personalization, and recommendation systems at scale.
• Apply NLP and generative AI to clinical workflows, including retrieval-augmented generation and agent patterns.
• Design product-facing AI architectures and foundational ML infrastructure.
• Establish technical standards for AI safety and guide how machine learning is introduced in a high-stakes clinical environment.
• Define technical strategy for MLOps, data pipelines, evaluation systems, and the broader AI/ML infrastructure ecosystem.
• Unblock complex engineering problems and mentor or multiply engineers across product areas.
• Ship applied AI features that improve match quality and user experience while maintaining trust and outcomes.

Requirements

• 10+ years of software engineering experience.
• 7+ years designing and scaling distributed systems.
• 5+ years building and deploying production-grade ML applications.
• 5+ years building and optimizing search, ranking, relevance, or recommendation engines at scale.
• 5+ years of strong Python programming and experience with at least one backend language, preferably TypeScript, Java, or Go.
• 2+ years building AI-powered products with foundation models such as OpenAI, Anthropic, or Gemini, including RAG and agent integration patterns.
• Proven experience with traditional search and retrieval infrastructure and modern vector databases.
• 3+ years of experience with MLOps, data pipelines, and rigorous offline and online evaluation systems.
• Ability to define technical strategy and guide AI/ML infrastructure development.

Nice-to-haves

• Experience architecting secure and compliant AI solutions in regulated environments, including HIPAA and GDPR.
• Familiarity with human-in-the-loop systems and clinical decision-support frameworks.
• Experience designing evaluation pipelines for human alignment, factual accuracy, or model interpretability.
• Contributions to open-source AI frameworks or applied research in NLP, healthcare AI, or generative AI safety.
• Experience contributing to early-stage team growth from 0 to 1.
• Experience leading or mentoring engineering teams in AI, ML platforms, or applied research.

Compensation and Benefits

• Salary range: $282,000–$370,650.
• 100% remote work environment for employees based in the United States; Hawaii is currently excluded.
• Medical, dental, vision, life, disability, and FSA/HSA benefits.
• 401(k) plan access.
• Generous paid time off, including two company-wide shutdown weeks each year for most employees.
• Paid parental leave for all parents.
• Employee Assistance Program.
• Quarterly department stipend for team-building activities or gatherings.
• Community and employee resource groups.
• New-hire home-office stipend and $50 monthly internet or cell-phone stipend.
• Wellness initiatives and a $50 monthly wellness stipend.