Staff Software Engineer, Play Games Discovery

Google Google · Big Tech · Bengaluru, Karnataka, India

Staff Software Engineer at Google Play focused on building personalized experiences for gamers using AI/ML recommendations. The role involves technical ownership, leadership, and designing/implementing ML solutions, optimizing infrastructure, and guiding model optimization and data processing. Experience in personalization, search quality, ranking, and recommendation systems is required.

What you'd actually do

  1. Take foundational technical ownership of high risk, ambiguous initiatives (e.g., evaluating native vs. hybrid web architectures, data-passing stacks, and cross-feature telemetry) by owning the deep technical spikes, architectural trade-offs, and complex code reviews.
  2. Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
  3. Facilitate alignment and clarity across teams on goals, outcomes, and timelines. Influence and coach a distributed team of engineers.
  4. Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.

Skills

Required

  • software development
  • software products
  • software design and architecture
  • Speech/audio
  • reinforcement learning
  • ML infrastructure
  • ML design
  • ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • personalization
  • search quality
  • ranking

Nice to have

  • data structures and algorithms
  • technical leadership
  • building large-scale data pipelines
  • online serving infrastructure
  • recommendation systems
  • ML systems for recommendations or search
  • software or physical storefront

What the JD emphasized

  • specialized ML areas
  • optimize ML infrastructure
  • model optimization
  • data processing strategies
  • personalization
  • search quality
  • ranking
  • recommendation systems
  • ML design
  • ML infrastructure

Other signals

  • building personalized experiences
  • AI/ML recommendations
  • optimize ML infrastructure
  • model optimization
  • data processing strategies
  • personalization
  • search quality
  • ranking
  • recommendation systems