חזרה

Backend Team Lead

Nanit
שכר לא צויןRamat Gan (Hybrid), היברידילידמשרה מלאה
משרה חיצונית, ההגשה באתר החברהאושר שהמשרה פתוחה לפני 9 שעות

Lead backend and MLOps engineers building the services, APIs, and data pipelines behind Nanit’s AI-driven product features. You will guide architecture, take models from experimentation to production, and improve the reliability of systems used to deliver insights to families.

מה תעשו

  • Manage and coach backend and MLOps engineers, supporting delivery, decision-making, and career growth.
  • Build and scale data pipelines with the AI group, and design how model insights are stored, queried, and exposed to the product.
  • Partner with the Algorithms team to build validation pipelines and move models from experiment to production.
  • Guide backend architecture decisions and collaborate with the platform team on infrastructure, deployment, and reliability standards.
  • Improve stability, observability, and alert quality; lead debriefs and follow up on action items.

דרישות

  • 8+ years of backend engineering experience and proven people leadership as a manager or team lead.
  • Production experience with web services/APIs on AWS, PostgreSQL, Redis, S3, Kubernetes, and Kafka, including failure handling.
  • Experience with batch or streaming data pipelines and monitoring or observability tools, including dashboards, logs, metrics, tracing, alerting, and on-call.
  • Ability to mentor and grow engineers, learn unfamiliar domains, and communicate clearly with technical and non-technical partners.

יתרון

  • Familiarity with the ML model lifecycle.
  • Python or exposure to Python-based research teams.
  • Big data infrastructure, Computer Vision, or Deep Learning experience.
  • Hands-on use of AI coding tools.

תנאי סף

  • 8+ years of backend engineering experience
  • Proven people leadership as a manager or team lead

הטבות

  • סביבת עבודה גמישה ופתוחה.
  • איזון בין עבודה לחיים אישיים.
  • השקעה מתמשכת בהתפתחות ובקידום העובדים.
Backend EngineeringAWSPostgreSQLRedisKubernetesKafkaData PipelinesMLOps