Research Engineer 4/5 – AI for Member Systems

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

Research Engineer at Netflix to apply ML expertise to design, develop, and scale personalization systems and algorithms for member experiences. This role involves creating production-ready ML solutions, optimizing models, and conducting experiments to improve key business metrics.

What you'd actually do

  1. Collaborate with cross-functional teams, including researchers, engineers, data scientists, and product managers, to develop and implement machine learning algorithms that improve personalization, recommendations, and member experiences.
  2. Create scalable, production-ready ML solutions, taking algorithms from initial concept through to deployment in Netflix's large-scale, real-time systems.
  3. Optimize the performance and scalability of machine learning models, ensuring they can handle the diverse tastes and behaviors of our global member base.
  4. Design and conduct offline experiments and A/B tests to validate the impact of algorithmic changes on key business metrics.
  5. Contribute to the ongoing improvement of our ML infrastructure and tooling, ensuring that we stay at the cutting edge of industry practices.

Skills

Required

  • Python
  • Scala
  • Java
  • C++
  • C#
  • machine learning algorithms
  • frameworks
  • training models
  • tuning models
  • deploying models in production

Nice to have

  • personalization systems
  • search engines
  • large-scale machine learning applications
  • neural networks
  • natural language processing
  • causal inference
  • open-source projects
  • cross functional teams

What the JD emphasized

  • 5+ years of experience in applying machine learning in an industrial setting
  • track record of delivering impactful results
  • Expertise in machine learning algorithms and frameworks
  • hands-on experience in training, tuning, and deploying models in production environments

Other signals

  • production-ready ML solutions
  • scale solutions
  • optimize performance and scalability
  • impact of algorithmic changes