As a Backend Engineer on the Personalization team, you'll develop and maintain the systems that power real-time recommendations in one of the most frequently used interfaces across the platform. Your work will directly influence what users see and discover, ensuring relevance and engagement through scalable, high-performance backend services.
What You’ll Do
- Design and operate backend systems that rank and deliver personalized content to a global user base, with a focus on low latency and high reliability.
- Collaborate with machine learning engineers to integrate trained models into production, ensuring efficient serving and seamless feature pipeline delivery.
- Work within an AI-native development environment—orchestrating, reviewing, and refining code generated by AI agents, while establishing patterns and safeguards to maintain system integrity.
- Make system-level decisions informed by user impact, measuring success through engagement, discovery, and product metrics.
- Define testing strategies, architectural standards, and review practices that support safe, scalable evolution of the codebase by both humans and AI agents.
- Run and interpret A/B tests, using data to guide deployment and iteration.
- Take ownership of service reliability, including monitoring, incident response, and on-call responsibilities.
Who You Are
- Experienced in building and maintaining backend services using Java or another JVM language in a distributed, microservice-based environment.
- A systems thinker who considers failure modes, service contracts, and architectural tradeoffs before writing code.
- Comfortable working with AI-powered development tools, capable of breaking down complex tasks, managing parallel agent workflows, and critically evaluating outputs.
- Product-focused, with a clear understanding of how backend decisions affect user experience and engagement.
- Familiar with real-time recommendation systems, ML serving infrastructure, and the performance demands of large-scale platforms.
- Committed to operational excellence, treating monitoring, alerting, and incident response as core responsibilities.
- Adaptable and proactive, thriving in environments where best practices are still emerging and collaboration spans engineering, data science, and product.
