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Rula is seeking a Principal AI Engineer to own applied AI and ML solutions across mental healthcare delivery. This is a deeply hands-on role focused on building production-grade systems that power patient-provider matching, clinical workflows, and patient engagement—not isolated prototypes.
You will develop and optimize advanced recommendation engines, retrieval models, and ranking systems that directly improve match quality and clinical outcomes. The scope spans Learning-to-Rank algorithms, semantic embeddings, LLM integration (RAG, agents), NLP applications in clinical workflows, and foundational ML infrastructure. You'll design both product-facing AI architectures and core ML platforms, set technical standards for AI safety in a regulated healthcare environment, and act as a technical multiplier for engineers across product surfaces.
Key responsibilities include: developing and fine-tuning models for search ranking and personalization; designing scalable ML infrastructure; establishing MLOps practices and rigorous evaluation systems (offline metrics like NDCG/MAP, online A/B testing); integrating foundation models responsibly into high-stakes clinical workflows; and shaping the broader AI ecosystem strategy. You'll work at the intersection of applied research, system architecture, and real-world clinical constraints, ensuring AI solutions enhance care accessibility and effectiveness while maintaining trust and compliance (HIPAA, GDPR).
Required: 10+ years software engineering (7+ distributed systems, 5+ production ML); 5+ years building search/ranking/recommendation engines at scale; 5+ years Python and backend language proficiency; 2+ years building with foundation models and LLM patterns; experience with search infrastructure (Elasticsearch, OpenSearch) and vector databases (Pinecone, Weaviate, FAISS, Milvus); 3+ years MLOps, data pipelines, and evaluation systems.
Preferred: experience in regulated environments; human-in-the-loop systems; evaluation for alignment and interpretability; open-source contributions; early-stage team growth; mentoring engineering teams.
