Software Engineer, Content Safety Team

Google Google · Big Tech · Singapore

Software Engineer on the Content Safety Team at Google, focusing on building and scaling systems to protect users and products from harmful content. This involves working with classifiers, vector databases, multimodal models, and agentic workflows, as well as model training and evaluation pipelines. The role requires experience with production-grade distributed systems and ML concepts, with a focus on responsible AI principles and ensuring the safety of Generative AI experiences.

What you'd actually do

  1. Participate in the design, build, and scale content safety systems including classifiers, vector databases, and multimodal models to protect business-critical products and Generative AI (GenAI) experiences.
  2. Develop and maintain production-grade distributed systems and content processing pipelines optimized for high throughput and reliability across server-side and on-device environments.
  3. Model training, evaluation, and productionization workflows, incorporating feedback loops and automation to continuously improve model quality and performance.
  4. Implement agentic workflows and advanced heuristics for deep threat understanding, enabling the proactive detection of complex abuse patterns.
  5. Contribute to agile engineering efforts to identify and mitigate novel abuse patterns, ensuring Google’s products remain engaged and safe in a shifting threat landscape.

Skills

Required

  • software programming in Python or C++
  • data structures and algorithms
  • implementing core ML concepts

Nice to have

  • safety-adjacent domains, including factuality, product policy, or broader responsible AI frameworks
  • designing and deploying global-scale defensive architectures and pipelines capable of meeting Service Level Objectives (SLOs)
  • managing safety for User Generated Content (UGC) or GenAI products
  • understanding of adversarial incentives, abuse vectors, and distribution dynamics like virality
  • high-level understanding of machine learning and Large Language Model (LLM) architecture, specifically transformers, activations, and the requirements for efficient, large-scale training and deployment
  • managing technical debt, reducing bug counts, and mitigating SLO breaches to maintain high operational standards

What the JD emphasized

  • content safety
  • Generative AI (GenAI) experiences
  • agentic workflows
  • transformer architecture
  • responsible AI principles
  • multimodal models
  • vector databases
  • Model training, evaluation, and productionization workflows

Other signals

  • content safety systems
  • Generative AI (GenAI) experiences
  • agentic workflows
  • transformer architecture
  • responsible AI principles