Senior Engineer, Machine Learning

This is a fully remote position, open to applicants in United States.

📋 Description

• Transform ambiguous clinical and operational inquiries into clearly defined ML tasks and establish robust baselines.

• Develop predictive models that enhance member care and collaborate with product engineering to provide insights to members and care teams.

• Create retraining triggers, implement drift and anomaly detection, and establish continuous evaluation mechanisms to maintain model integrity in production.

• Design both offline and online evaluations while conducting error analysis on model performance issues.

• Construct causal models that are validated through clinical experiments.

• Collaborate with Product Analytics on CUPED/CUPAC variance-reduction strategies.

• Generate ML- and GenAI-derived insights for personalized coaching and member experiences.

• Direct complex ML projects from problem identification through to production implementation.

• Manage technical decisions and dependencies across Applied AI, ML Ops, Product Engineering, and Product Analytics.

• Disseminate knowledge and best practices that enhance model quality and delivery.

⛳️ Requirements

• Over 5 years of experience in building, evaluating, and deploying production-level Machine Learning systems for practical applications.

• Solid grounding in traditional ML techniques, including predictive modeling, tree-based models like XGBoost, and time-series analysis on structured longitudinal data.

• Expertise in rigorous evaluation and experimental design, emphasizing statistical reasoning, offline/online evaluation, and the identification of leakage, selection bias, and confounding factors.

• Capability to articulate model behavior, trade-offs, uncertainties, and limitations to both technical and non-technical audiences.

• Proficient in designing and analyzing experiments to establish causation instead of mere correlation.

• Specialized knowledge in one or more areas, including causal ML and experimentation, recommendation/ranking systems, predictive modeling, or NLP/LLM-based systems.

• Commitment to the responsible use of AI-assisted development.

• Preferred experience in clinical or health-tech ML applications.

• Familiarity with PHI, HIPAA-compliant systems, or other regulated data is preferred.

• Sound judgment on when traditional ML is more effective than LLMs and when to utilize an LLM.

• Must be eligible to work in the United States.

• Adherence to Virta's security and privacy protocols for sensitive patient information governed by HIPAA.

🏝️ Benefits

• Equity.

• Remote-first work structure.

• Office hubs located in Denver and San Francisco.

• Training in security and privacy.

• Compensation structure based on location.