Data Engineer (L5) - Content Machine Learning

Posted 4 Days Ago
Los Angeles, CA, USA
In-Office
170K-720K Annually
Mid level
News + Entertainment
The Role
Build and scale web‑scale data pipelines and ML systems for Netflix's Knowledge Graph. Process structured and unstructured data, apply information extraction and graph ML, ensure data quality, and collaborate with ML scientists and business partners to deliver productionized data and ML products.
Summary Generated by Built In

Netflix is one of the world’s leading entertainment services with 278 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.

The Role

Netflix is the world's leading internet streaming service with over 250 million members in 190 countries. Our members enjoy a wide variety of streamed videos per day, using over 1000 types of devices, generating more than a third of downstream Internet traffic in North America. The Content Knowledge Graph team within Data Science & Engineering equips scientists, engineers, and business executives with a global understanding of the entertainment industry “on & off-service”. Our flagship product, Netflix KG (“Hitch”), a data and analytics foundational layer, is a data pillar for 100s of diverse use cases across Netflix: from enabling content and marketing business executives to make better data-driven decisions to equipping AI/ML scientists with signals that power billions of daily predictions, to providing key inputs to engineering teams building business-critical applications. Hitch is a foundation for innovation that is helping to fuel Netflix’s long-term growth. 

We are looking for a Data Engineer with a background in Machine Learning to own and scale our data systems while supporting high-visibility data pipelines and products. As part of this team, you will work on a diverse tech stack to build insightful, scalable, and robust data systems and machine learning models that create and describe the entities, interactions, and usage patterns across the Netflix Knowledge Graph. You will collaborate closely with other Machine Learning Engineers and Scientists to build scalable graphs and then leverage graph data in ML predictive models.

The ideal candidate will have a strong background in distributed data processing and can demonstrate strong data intuition and end-to-end ownership of our systems - from data collection and processing to running ML systems in production.

Who you are:

  • Passionate about data systems: build reliable and trustworthy web-scale data products 
  • Highly proficient in at least one of the programming languages (e.g. Python, Java, or Scala) with at least 4 years of software/data engineering experience
  • Comfortable with SQL and using big data technologies (e.g. Hive, Presto, Spark, Iceberg, etc) on medium to large-scale data.
  • Familiarity with machine learning libraries TensorFlow, PyTorch, JAX, or Keras
  • Experience working in machine learning or natural language processing domains. Some experience in implementing and productionizing models in large-scale industrial settings is a plus. 
  • Conceptually familiar or interested in learning graph technologies (e.g. databases, data models, query languages, graph machine learning)
  • You have strong communication skills and can collaborate with cross-functional team members and partners on projects that drive high-impact outcomes
  • Excited about learning new business domains, demonstrating excellence, and growing your career 

What you will do:

  • Engineer efficient, adaptable, and scalable data pipelines to process structured and unstructured data 
  • Leverage ML techniques for information extraction, building knowledge graphs with the information, and leveraging graph ML to power business insights.
  • Build robust data quality systems using best-in-the-class data and ML techniques enabling the team and partners to deliver more business value
  • Develop a deep understanding of the Netflix Data Science & Engineering ecosystem and related business domains 
  • Understand business requirements and provide data that empowers teams to innovate on top of the Netflix KG
  • Join a stunning team of data and machine learning engineers with diverse skill sets, partnering closely with data science, analytics, and business counterparts

Our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $170,000 - $720,000.

Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more detail about our Benefits here.

Netflix is a unique culture and environment. Learn more here.

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity of thought and background 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.

Skills Required

  • Proficiency in at least one language: Python, Java, or Scala with at least 4 years of software/data engineering experience.
  • Comfortable with SQL and big data technologies (Hive, Presto, Spark, Iceberg) on medium to large-scale data.
  • Familiarity with machine learning libraries: TensorFlow, PyTorch, JAX, or Keras.
  • Experience working in machine learning or natural language processing domains.
  • Experience implementing and productionizing ML models in large-scale industrial settings.
  • Conceptual familiarity or interest in learning graph technologies (graph databases, graph ML, query languages).
  • Strong communication skills and ability to collaborate with cross-functional teams.

Netflix Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Netflix and has not been reviewed or approved by Netflix.

  • Fair & Transparent Compensation Compensation is positioned as “personal top of market” with regular recalibration and broad posted ranges for senior roles that signal the philosophy. The cash‑forward structure and clearly described pay‑mix choices help set expectations on how pay is determined.
  • Equity Value & Accessibility Employees can choose the mix of cash versus fully vested 10‑year stock options, with grants structured to be retained even after departure. This employee‑directed design increases accessibility and control over equity participation.
  • Healthcare Strength Health coverage is described as comprehensive across medical, dental, vision, and mental health, with employer funding designed to offset premiums. Additional resources like counseling/coaching and wellness support reinforce breadth in care access.

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The Company
HQ: Los Gatos, CA
13,212 Employees
Year Founded: 1997

What We Do

Netflix is the world's leading streaming entertainment service with 209 million paid memberships in over 190 countries enjoying TV series, documentaries and feature films across a wide variety of genres and languages. Members can watch as much as they want, anytime, anywhere, on any internet-connected screen. Members can play, pause and resume watching, all without commercials or commitments.

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