Member of Technical Staff - Imagine Safety

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

This role focuses on building and scaling safety systems for a multimodal generative AI platform (Grok), ensuring responsible deployment of images, video, and audio generation. It involves designing safeguards, detection systems, and evaluation frameworks to prevent harm, mitigate risks, and improve safety through feedback loops, while also ensuring high performance and low latency.

What you'd actually do

  1. Design and implement scalable safety systems for Grok’s media generation platform, including real-time content moderation, risk detection, and safeguard enforcement for images, video, and audio.
  2. Build infrastructure to measure, monitor, and mitigate safety risks such as harmful content, bias, deepfakes, intellectual property issues, and misuse at global scale.
  3. Develop tools, pipelines, and evaluation frameworks that enable rapid iteration on safety policies in collaboration with researchers, product, and policy teams.
  4. Architect robust feedback loops between user interactions, model outputs, and training data to continuously improve safety while maintaining high performance and low latency.
  5. Own full-cycle development of safety features: from problem definition and prototyping to deployment, monitoring, incident response, and long-term refinement.

Skills

Required

  • Rust
  • production safety systems
  • trust & safety systems
  • content moderation systems
  • real-time detection systems
  • data pipelines
  • evaluation frameworks
  • high-throughput AI applications
  • robust solutions
  • reliable solutions
  • high standards of uptime
  • high performance
  • problem-solving skills
  • responsible AI development

Nice to have

  • multimodal content safety
  • generative AI safety
  • machine learning classifiers
  • safety evaluation
  • red-teaming
  • adversarial testing
  • distributed systems
  • real-time inference serving
  • Kubernetes
  • observability tools
  • large-scale data infrastructure
  • content moderation
  • anti-abuse
  • model alignment
  • responsible AI

What the JD emphasized

  • production safety
  • consumer-facing products at scale
  • real-time detection systems
  • high-throughput AI applications
  • millions of users
  • multimodal content safety
  • generative AI safety in production environments
  • safety evaluation
  • large-scale data infrastructure
  • safety-critical features

Other signals

  • building safety systems
  • evaluating AI models
  • responsible AI deployment
  • multimodal generation safety