Software Engineering Manager, Content Safety, Infra

Google Google · Big Tech · Singapore

Software Engineering Manager for Content Safety, focusing on AI/ML infrastructure and deployment for protecting users from harmful content. The role involves leading teams, setting technical direction, and ensuring the scalability and reliability of ML systems.

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
  • ML field specialization
  • leading ML design
  • optimizing ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • technical leadership
  • people management
  • team leadership

Nice to have

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

What the JD emphasized

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

Other signals

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