Senior Engineer II, Machine Learning

Virta Health is hiring a Senior Engineer II, Machine Learning for its Applied AI team. The role leads the design and production deployment of predictive ML and GenAI platform capabilities, driving clinical impact through causal modeling, experimentation, technical architecture, and cross-functional collaboration.

Responsibilities:

• Business Problem Framing: Independently translate complex clinical and operational business problems into cohesive Applied AI roadmaps. Align leadership, product squads, and cross-functional partners around strategic ML and GenAI investments tied to company-level OKRs
• Causal ML & Intervention Effectiveness: Lead the design and implementation of causal ML strategies, validating models against real clinical experiments to quantify what types of coaching or digital interventions work best for specific member segments
• Model & Signal Integration: Design predictive ML models and GenAI-derived signals (e.g., conversation subtext analysis) that feed as clean, modular inputs into product features
• GenAI Platformization: Guide the evolution of monolithic GenAI applications into reusable platform capabilities that other product engineering teams can leverage. Examples may include member context retrieval, safety/compliance guardrails, treatment-guideline grounding, and chatbot orchestration
• Cross-Pillar Technical Architecture: Own technical architectures spanning traditional ML and GenAI platformization. Lead discovery and design for high-leverage initiatives like Next Best Action support for care teams and triage
• Scientific Rigor & Experimentation: Partner closely with Product Analytics to apply advanced variance-reduction techniques (CUPED/CUPAC) and continuous evaluation frameworks, sharpening experiment sensitivity and accelerating experiment cycle times
• Technical Standards & Mentorship: Set and enforce high standards for AI/ML engineering, code quality, model evaluation, and safety guardrails across the org

Requirements:

• 8+ years of experience developing and deploying production-grade Machine Learning systems, with proven technical leadership across multi-quarter initiatives
• Experience applying causal inference and causal ML frameworks (e.g., Double ML, CATE estimation, instrumental variables, or uplift modeling) to real-world observational and experimental data
• Strong foundation across traditional ML (predictive modeling, tree-based models like XGBoost, time-series analysis on structured longitudinal data)
• Experience building modern GenAI architectures (LLM integration, RAG, evaluation, safety guardrails)
• Outstanding technical communication skills: ability to translate complex AI concepts into clear business strategy for senior executives, product partners, and clinical stakeholders
• Experience establishing responsible, high-leverage practices for AI-assisted development
• Background in clinical or health-tech ML, patient outcomes prediction, risk stratification, biomarker modeling, or similar domains
• Experience designing reusable platform capabilities or shared services, rather than isolated single-use models or point applications
• Prior experience leading or managing ML teams, with a desire to return to deep technical impact as a senior individual contributor