Machine Learning Scientist 5 - Games

Netflix Netflix · Big Tech · United States · Remote · Data & Insights

Machine Learning Scientist 5 focused on forecasting and audience research within Netflix Games. The role involves building foundational ML building blocks like embeddings and models, accelerating product development through tools and pipelines, bridging the Netflix ecosystem, designing scalable ML pipelines, and establishing technical standards for ML application in game domains. Requires a PhD and significant experience in leading end-to-end ML projects and navigating large technical organizations.

What you'd actually do

  1. Build Foundational ML Building Blocks: Develop sophisticated embeddings and models that incorporate deep game-specific signals to solve high-impact business problems, including audience insights, opportunity sizing, and forecasting.
  2. Accelerate Product Development: Build the tools, models, and pipelines required to accelerate DSE workflows across games portfolio, studios, product, and platform.
  3. Bridge the Netflix Ecosystem: Act as a key liaison with the broader Netflix DSE and AI teams to adopt, adapt, and tailor global Netflix capabilities for the unique requirements of the gaming space.
  4. Design Scalable Pipelines: Create end-to-end ML pipelines that accelerate and enable DSE members across games to uncover actionable insights and build data-intensive game features.
  5. Elevate ML Practices: Establish the technical standards for how ML capabilities are applied across game domains.

Skills

Required

  • ML model development
  • embeddings
  • forecasting
  • audience insights
  • ML pipelines
  • causal inference principles
  • Python
  • PyTorch

Nice to have

  • game development teams
  • production-grade ML systems
  • MLOps best practices
  • evaluation frameworks
  • LangChain

What the JD emphasized

  • Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
  • 5+ years of experience leading complex, end-to-end ML projects that impact end-customer experiences.
  • 3+ years of experience navigating large-scale technical organizations to align roadmap priorities and share infrastructure

Other signals

  • Develop sophisticated embeddings and models that incorporate deep game-specific signals to solve high-impact business problems, including audience insights, opportunity sizing, and forecasting.
  • Build the tools, models, and pipelines required to accelerate DSE workflows across games portfolio, studios, product, and platform.
  • Create end-to-end ML pipelines that accelerate and enable DSE members across games to uncover actionable insights and build data-intensive game features.
  • Establish the technical standards for how ML capabilities are applied across game domains.