Teams at Bevel (bevel.health)

Design, build, and run large-scale backend and data infrastructure; productionize and run experiments on AI/ML models; implement MLOps best practices (versioning, monitoring, alerting); improve model accuracy, speed, and cost; and drive engineering process and culture improvements.

Build, ship, and operate backend systems and data infrastructure; develop robust business logic for health data; prototype and productionize experiments to improve AI/ML models; establish scalability, security, testing, and MLOps best practices; and iterate on engineering process and culture.

The Backend Engineer will design, build, and improve backend systems, focusing on health data processing and AI/ML model efficiency.