Software Engineer 6 - Evidence Engineering

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

Staff-level Software Engineer for Netflix's Evidence Engineering team, focusing on building and operating large-scale, low-latency distributed systems for real-time asset delivery and personalization. The role involves technical leadership, platform evolution, and collaboration with ML scientists and product teams to enhance customer-facing experiences.

What you'd actually do

  1. Lead the technical vision for core Evidence capabilities, setting the technical direction and driving longer-term strategy for asset delivery, personalization infrastructure, and merchandising capabilities. Ensuring low latency, high availability, and cost-efficient operation across all device platforms.
  2. Partner closely with operations, ML scientists, and merchandising teams to design and build state-of-the-art self-service tools that enable teams to experiment and launch the best experiences at scale.
  3. Collaborate with Personalization, Content Engineering, Product, and Platform Infrastructure to deliver well-integrated solutions that meet the needs of multiple internal consumers.
  4. Identify and address gaps in reliability, observability, and scalability before they become incidents — and raise the operational bar for the broader team.
  5. Navigate open-ended technical problems with limited precedent, bringing structure and clarity to ambiguous situations and building alignment across diverse stakeholders.

Skills

Required

  • Extensive experience building and operating large-scale, low-latency distributed systems, ideally on horizontal platforms that serve multiple engineering teams simultaneously.
  • Deep knowledge of real-time asset delivery or content serving systems, with a track record of improving both performance and cost efficiency at scale.
  • Strong judgment in system design — you can identify the right trade-offs between consistency, availability, and latency, and communicate those decisions clearly to technical and non-technical stakeholders.
  • Experience working at the boundary between backend infrastructure and ML or personalization systems, with an understanding of how data pipelines and model outputs translate into product experiences.
  • A proven ability to set technical direction, drive multi-quarter roadmaps, and deliver high-impact projects across organizational boundaries.
  • Strong communication and collaboration skills — you build trust with partners across engineering, product, and design, and can influence direction without formal authority.
  • The ability to operate with high autonomy in ambiguous environments and deliver results independently.

Nice to have

  • Working knowledge of GenAI techniques applied to engineering workflows is a strong plus — we're actively looking for engineers who use these tools to raise the team's overall productivity and technical velocity.

What the JD emphasized

  • low-latency
  • high-throughput
  • real-time personalization
  • large-scale
  • horizontal platforms
  • low-latency
  • high-throughput
  • real-time asset delivery
  • performance
  • cost efficiency
  • system design
  • consistency, availability, and latency
  • backend infrastructure and ML or personalization systems
  • data pipelines and model outputs
  • multi-quarter roadmaps
  • high-impact projects
  • ambiguous environments