Full Stack Software Engineer 4 - Localization Engineering
Netflix
- Location
- Los Gatos,California,United States of America
- Work model
- On-Site
- Level
- Mid
- Salary
- $250k – $413k/yr
- H-1B history
- 80 approvals (FY2023)
Skills
About this role
At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.
At Netflix, localization isn't simply about translation. It's how we make stories feel native to audiences around the world. Every subtitle, dub, title treatment, artwork, and localized experience helps millions of members connect with content in their own language and culture.
The Logistics Management squad is the operational backbone of Netflix's localization platform. We own the tools, workflows, and infrastructure through which every piece of content is ordered, tracked, fulfilled, and paid for — across 2,700 weekly internal users and 180+ external vendors.
Our mission is to build the platform layer that makes AI-first localization possible. The 18-24 month vision is to automate 70-80% of localization workflows so that human effort shifts from execution to governance and the highest-impact decisions. We are the team that makes that shift real: every workflow we automate, every manual handoff we eliminate, and every vendor dependency we reduce is a step toward localization operating at internet speed.
Critically, localization is not downstream of Netflix's content pipeline — it is inside it. Content cannot play for international audiences until it is localized.
Qualifications
* Deep experience building complex, high-quality user interfaces with HTML, JavaScript/TypeScript, and CSS, with expert-level proficiency in React and modern frontend architecture (component design, state management, performance optimization)
* A strong eye for UI/UX craft. You build interfaces that are clear, predictable, and hold up reliably for enterprise users who rely on them daily
* Experience designing and consuming GraphQL and/or RESTful APIs to power rich client experiences, and comfortable working across the stack, including services built in Java or similar OO languages
* A proven track record of building resilient, high-scale, low-latency services in production, and translating business requirements into technical designs and data models
* Work cross-functionally with design, product, and engineering partners to build, test, deploy, and launch UIs that operationalize our localization workflows at scale
What Sets You Apart
* You’re a self-starter who takes initiative and drives work forward without needing significant direction
* You proactively use AI tools to accelerate your workflow, learn faster, and amplify your impact
* You communicate clearly and are comfortable navigating ambiguity. You ask the right questions, seek out the right context, and make good judgment calls without complete information
* You are product-minded. You're curious about the "why" behind what you're building, and you bring your own ideas and opinions to the table rather than just implementing a spec as given
* You take a thoughtful, practical approach to problem-solving that considers tradeoffs and avoids over-engineering
* You take an interest in how people actually use what you build, and look for ways to get early feedback and validate your work rather than shipping and moving on
* You evangelize new ideas and exemplify technical leadership with a bias for action.
* You appreciate the complexity of engaging with a global challenge, and are passionate about crafting interfaces that feel native, no matter the language or locale
* You are a resourceful engineer who can independently identify the root cause of issues and implement dependable, maintainable solutions across the entire stack
Bonus Points
* Experience applying AI in production systems, e.g., integrating LLMs into user-facing or internal tools and able to identify where applied AI can