Senior Engineer, Machine Learning
New
V
Virta HealthHealth-tech
Location: Remote; Workplace: RemoteFull-TimeSenior
Salary$161,500 to $232,500; Offers Equity
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Job Details
- Experience
- 5+ years building, evaluating, and deploying production-grade Machine Learning systems for real-world use cases.
- Required Skills
- Machine LearningNLP
Requirements
- Have 5+ years of experience building, evaluating, and deploying production-grade machine learning systems for real-world use cases.
- Bring a strong foundation in traditional ML, including predictive modeling, tree-based models such as XGBoost, and time-series analysis on structured longitudinal data.
- Apply statistical reasoning and rigorous experimental design, including offline and online evaluation.
- Identify leakage, selection bias, and confounding in messy real-world data.
- Explain model behavior, tradeoffs, uncertainty, and limitations to technical and non-technical stakeholders.
- Design and analyze experiments to distinguish what drives an outcome from what is merely correlated with it.
- Have depth in one or more of causal ML and experimentation, recommendation/ranking systems, predictive modeling, or NLP/LLM-based systems.
- Use AI-assisted development responsibly to accelerate and improve model development.
- Nice to have: experience with clinical or health-tech ML data such as claims, EHR, clinical notes, scheduling, utilization, or member engagement data.
- Nice to have: experience with PHI, HIPAA-aware systems, or other regulated data.
Responsibilities
- Turn ambiguous clinical and operational questions into well-scoped ML tasks and build strong baselines.
- Build predictive models for member care, including disengagement risk, coach outreach, and health trends.
- Partner with product engineering to deliver model signals to members and care teams.
- Build model-specific production logic for retraining triggers, drift and anomaly detection, and continuous evaluation.
- Design offline and online evaluations, select metrics for imbalanced, delayed, and noisy healthcare outcomes, and analyze model failures.
- Build causal models validated against clinical experiments and partner with Product Analytics on CUPED/CUPAC variance-reduction techniques.
- Build ML- and GenAI-derived signals for personalized coaching and member experiences.
- Lead ML initiatives from problem framing through production, coordinating decisions and dependencies across teams.
- Share practices that strengthen model quality and delivery across the team.
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