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Netflix

Full-Stack Engineer L5, ML Insights & Visualizations, Machine Learning Platform

Posted 12 Days Ago
Remote
Hiring Remotely in USA
100K-558K Annually
Mid level
Remote
Hiring Remotely in USA
100K-558K Annually
Mid level
Develop and maintain web-based tools for AI/ML practitioners, improve model observability, enhance user experience, and collaborate cross-functionally. Drive product excellence and improve systems and codebase.
The summary above was generated by AI

Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time.

Machine Learning/Artificial Intelligence powers innovation in all areas of the business, from helping members choose the right title for them through personalization, to enhancing our understanding of our audience and content slate, to optimizing our payment processing and other revenue-focused initiatives. Building highly scalable and differentiated ML infrastructure is key to accelerating this innovation.

The Opportunity

The ML Insights & Visualizations team builds powerful tools and platforms that enable hundreds of AI/ML practitioners across Netflix to develop, deploy, and monitor business-critical models. Our mission is to deliver seamless, intuitive, and insightful experiences that boost productivity and innovation for teams working on personalization, growth, commerce, ads, content, and studio AI/ML algorithms.

We are seeking talented Full-Stack Engineers to join us in developing end-to-end solutions that span UI, backend, and data layers. You’ll work closely with product managers, ML engineers, data scientists, and other cross-functional partners to create robust observability and visualization workflows for cutting-edge AI/ML models, including bandits, multi-task learning models, Large Language Models (LLMs), and other foundation models. 

What You’ll Do
  • 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.

  • Enhance Observability: Build and improve dashboards for model observability, anomaly and drift detection, cost monitoring, and system health.

  • Improve User Experience: Collaborate with users and stakeholders to gather feedback and deliver intuitive, seamless, and impactful user experiences.

  • Drive Product Excellence: Continuously improve our systems, codebase, and team processes to enhance overall performance. Introduce and champion best practices in full-stack development.

  • 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.

Must-Have Skills
  • Full-Stack Development: Proficiency in modern UI frameworks (React preferred), JavaScript/TypeScript, Node.js, and building scalable backend systems (Java, Scala, or similar; Spring Boot or equivalent frameworks).

  • Cloud Experience: Hands-on experience with public cloud platforms (AWS, Azure, or GCP).

  • Data & Observability: Familiarity with ML model lifecycle management, logging, metrics, analytics, and building tools for data visualization and observability.

  • Legacy & Modernization: Experience in maintaining and improving legacy systems, with the ability to evaluate tradeoffs between refactoring, rebuilding, and buying solutions.

  • Strong Communication: Excellent written and verbal communication skills, with a proactive approach to cross-functional collaboration.

  • Education: BS/MS in Computer Science, Applied Math, Engineering, or a related field, or equivalent practical exp.

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.

About You
  • You’re passionate about empowering others and building tools that create leverage for ML/AI practitioners.

  • You thrive in ambiguous environments and can deliver results while investing in long-term solutions.

  • You’re collaborative, value diverse perspectives, and contribute to a culture of inclusion and excellence.

  • You’re curious, eager to learn new technologies and domains, and enjoy mentoring others.

Netflix Culture

Netflix’s culture is built on values of high performance, candor, context over control, and inclusion. We celebrate diversity and are committed to building teams with a wide range of backgrounds, perspectives, and skills.

We offer a flexible, market-driven compensation structure–choose your mix of salary and stock options, with a typical range for this role of 100K-558K. Learn more about our benefits and unique culture.

Ready to shape the future of ML/AI at Netflix?
Apply now and help us deliver world-class ML experiences to millions of members worldwide.

Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

Job is open for no less than 7 days and will be removed when the position is filled.

Top Skills

AWS
Azure
GCP
Java
JavaScript
Node.js
React
Scala
Spring Boot
Typescript

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