Software Engineering Manager, Content Safety, Infra

Google Google · Big Tech · Singapore

Software Engineering Manager for Content Safety, focusing on ML infrastructure and AI-based techniques to protect users from harmful content. The role involves managing engineers, setting team priorities, developing technical roadmaps, and overseeing the deployment of large-scale projects. Requires significant experience in ML infrastructure, model deployment, evaluation, data processing, and fine-tuning, with a strong emphasis on people management and technical leadership.

What you'd actually do

  1. Set and communicate team priorities that support the organization's goals. Align strategy, processes, and decision-making across teams.
  2. Set expectations with people based on their level and role and align to the organization's goals. Meet regularly with people to discuss performance and development and provide feedback and coaching.
  3. Develop the technical goal and roadmap within the scope of the teams. Evolve the roadmap to meet future requirements and infrastructure needs.
  4. Protect users from exposures to offensive, sensitive or harmful content, and unlock new opportunities for the business.
  5. Identify opportunities to mitigate internal signal developer issues, working with the community to prioritize and launch improvements.

Skills

Required

  • software development
  • Machine Learning (ML) infrastructure
  • model deployment
  • model evaluation
  • data processing
  • fine tuning
  • technical leadership
  • people management
  • team leadership

Nice to have

  • content safety applied to software products
  • Responsible AI
  • factuality
  • product policy
  • scaling pipelines
  • deploying systems
  • designing defensive architecture
  • Service Level Objectives (SLOs)
  • Large Language Models (LLMs)
  • transformers
  • activations
  • training and deploying LLMs

What the JD emphasized

  • Machine Learning (ML) infrastructure
  • model deployment
  • model evaluation
  • data processing
  • fine tuning
  • Large Language Models (LLMs)

Other signals

  • ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • fine tuning
  • LLMs
  • transformers
  • activations