Research Scientist 5 - Content Promotion and Distribution

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

Research Scientist at Netflix focused on developing and deploying AI/ML solutions for content promotion and discovery. The role involves end-to-end development, including model training, evaluation, and productization of vision-language and multimodal LLM systems, with a focus on advancing the state of the art and enhancing member experience.

What you'd actually do

  1. Lead the end-to-end process of AI/ML development and deployment spanning research, model training, offline and online evaluation, and large-scale productization
  2. Partner closely with other research scientists and engineers to effectively integrate models into business applications and platforms
  3. Serve as a cross-functional subject matter expert to surface high-impact opportunities, define roadmaps, and prioritize research investments; drive execution with engineering, product, and creative teams.
  4. Communicate complex concepts with both technical and non-technical stakeholders to influence strategic decisions
  5. Advance the state of the art and deliver innovations that enhance content discovery experience of our members.

Skills

Required

  • Python
  • PyTorch or Tensorflow
  • MS or PhD in ML, Computer Science or related fields
  • Strong AI/ML foundations
  • hands-on experience training, evaluating, and deploying vision-language models and multimodal LLM systems
  • Demonstrated research impact in computer vision and multimodal learning
  • Proven track record of bringing clarity and leading ambitious roadmaps to solve complex problems
  • Exceptional communication and collaboration skills

Nice to have

  • Experience in building agentic workflows and systems for multimodal search and understanding

What the JD emphasized

  • publications at top-tier conferences and journals
  • vision-language models and multimodal LLM systems
  • end-to-end process of AI/ML development and deployment

Other signals

  • end-to-end AI/ML development and deployment
  • model training, offline and online evaluation
  • large-scale productization
  • vision-language models and multimodal LLM systems
  • publications at top-tier conferences and journals