Full-stack Engineer 5 – AI Insights & Visualizations, AI Platform

Netflix Netflix · Big Tech · United States · Remote · Engineering

Full-Stack Engineer to build internal tools and platforms for AI/ML practitioners at Netflix, focusing on visualization, monitoring, and operation of AI/ML models and pipelines, including observability, anomaly detection, and cost monitoring.

What you'd actually do

  1. Design & Build End-to-End Solutions: Develop and maintain web-based internal tools and platforms that help AI/ML practitioners visualize, monitor, and operate AI/ML models and pipelines.
  2. Enhance Observability: Build and improve dashboards for model observability, anomaly and drift detection, cost monitoring, and system health.
  3. Improve User Experience: Collaborate with users and stakeholders to gather feedback and deliver intuitive, seamless, and impactful user experiences.
  4. Drive Product Excellence: Continuously improve our systems, codebase, and team processes to enhance overall performance. Introduce and champion best practices in full-stack development.
  5. Cross-Functional Collaboration: Partner with engineering, product, and research teams distributed across multiple US-based time zones to deliver impactful solutions and drive ML/AI innovation at Netflix.

Skills

Required

  • Full-Stack Development
  • React
  • JavaScript/TypeScript
  • Node.js
  • Java, Scala, or similar backend development
  • Spring Boot or equivalent frameworks
  • Public cloud platforms (AWS, Azure, or GCP)
  • ML model lifecycle management
  • logging
  • metrics
  • analytics
  • building tools for data visualization and observability
  • maintaining and improving legacy systems
  • Strong Communication

Nice to have

  • Experience building UI tools for ML practitioners or data scientists
  • Deep understanding of ML model development, deployment, and monitoring workflows
  • Track record of shipping and refining products based on user feedback
  • Passion for data-driven product development and improving engineering productivity

What the JD emphasized

  • building tools for AI/ML practitioners
  • observability
  • visualization

Other signals

  • building tools for AI/ML practitioners
  • observability and visualization workflows for AI/ML models
  • end-to-end solutions that span UI, backend, and data layers