Member of Technical Staff - Imagine Product

xAI xAI · AI Frontier · Palo Alto, CA · Product

This role focuses on building and scaling systems for AI-driven media experiences, specifically for Grok users. It involves designing and implementing scalable infrastructure for real-time multi-modal interactions, processing various media types, and collaborating with research and product teams to deliver consumer-facing features. The role emphasizes full-cycle development from design to deployment and monitoring, aiming to reach hundreds of millions of users.

What you'd actually do

  1. Design and implement scalable systems to support Grok's AI-driven media experiences, ensuring high performance, reliability, and low-latency at global scale.
  2. Architect robust infrastructure for real-time multi-modal interactions, including handling generation requests, media processing, and seamless integration with frontend and model serving layers.
  3. Build and optimize large-scale data pipelines to ingest, process, and analyze multi-modal data (images, video, audio), fueling continuous improvement and personalization of Grok's media capabilities.
  4. Collaborate closely with frontend engineers, AI researchers, and product teams to deliver captivating, media-rich features and end-to-end user experiences.
  5. Own full-cycle development of solutions: from system design and prototyping to deployment, monitoring, observability, and iterative refinement.

Skills

Required

  • Python or Rust
  • writing clean, efficient, maintainable, and scalable code
  • designing and building systems for consumer-facing products
  • performance
  • reliability
  • handling high-throughput workloads
  • large-scale data infrastructure and pipelines
  • multi-modal or media-heavy AI applications
  • delivering robust, production-grade solutions to millions of users
  • high standards of quality and uptime
  • problem-solving skills
  • turning innovative ideas into high-impact, scalable realities

Nice to have

  • real-time systems
  • inference serving
  • multi-modal data processing at scale
  • distributed systems
  • containerization (e.g., Kubernetes)
  • observability tools
  • performance tuning for AI workloads
  • AI-driven consumer products
  • media generation technologies
  • collaborating across engineering, research, and product teams

What the JD emphasized

  • high performance
  • reliability
  • low-latency
  • global scale
  • real-time multi-modal interactions
  • large-scale data pipelines
  • multi-modal data
  • production-grade solutions
  • millions of users
  • high standards of quality and uptime

Other signals

  • building and scaling robust, high-performance systems
  • powering immersive, multi-modal media interactions
  • turning advanced multimodal models into production-grade features
  • delivering production-ready, maintainable code that powers features reaching hundreds of millions of users