Netflix

Research Engineer 4/5 - Member Lifecycle and Monetization

Netflix$466K — $500K+*
US-AnywhereRemote in United States
Consumer Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Degree in Computer Science or related field
  • 4+ years of full-time engineering experience
  • Excellent software design and development skills in Scala, Java, and Python
  • Strong communication skills for explaining technical concepts
  • Broad understanding of machine learning concepts
  • Familiarity with end-to-end ML pipelines and challenges
  • Curiosity and motivation for solving open-ended challenges

Responsibilities

  • Design, implement, and operate impactful machine learning models
  • Collaborate with cross-functional teams to identify value-based ML applications
  • Develop scalable, production-ready ML solutions from concept to deployment
  • Enhance infrastructure for ML model development and deployment
  • Advocate for best practices in availability, scalability, and operational excellence

Benefits

  • Comprehensive health plans
  • Mental health support
  • 401(k) retirement plan with employer match
  • Stock option program
  • Flexible spending accounts
  • Family-forming benefits
  • Generous paid time off policies
Full Job Description

The Member Lifecycle and Monetization Data Science & Engineering team plays a critical role for Netflix in driving and accelerating sustainable growth of members and revenue globally, by leveraging data, experimentation & machine learning to develop compelling and persuasive conversion and monetization experiences post-signup to optimize revenue per member. Machine Learning in these areas is a relatively greenfield area, and comes with the potential for 0-1 applications that can drive millions of dollars of impact at Netflix’s scale. 

We are looking for a research engineer to join the team to contribute to operating, as well as innovating on growth and commerce algorithms in production, validating through running offline experiments, and building online A/B tests to run in production systems. You’ll partner with other ML engineers, scientists and product managers on cross-functional ML initiatives.

To excel in this role, you should have experience with large-scale applications involving machine learning, a good sense of software engineering principles and design, possess strong communication skills, and the ability to work well in large cross-functional teams.

In this role, you will:

  • Design, implement and operate high impact machine learning models 

  • Partner closely with cross-functional teams, including researchers, engineers, data scientists, and product managers, to identify high value applications of machine learning, translating business intuition into data-driven solutions 

  • Work closely with scientists and engineers to create scalable, production-ready ML solutions, taking algorithms from initial concept through to deployment in Netflix's large-scale, real-time systems.

  • Contribute to the development of better infrastructure for developing and deploying ML models

  • Advocate for and apply best practices when it comes to availability, scalability, operational excellence, and cost management

What you’ll bring:

  • A degree in Computer Science or a related field 

  • 4+ years of full time engineering experience 

  • Curious, self-motivated, and excited about solving open-ended challenges at Netflix.

  • Excellent software design and development skills in multi-language settings with Scala, Java, and Python and software engineering best practices (e.g. version control, testing, code review, etc.)

  • Exceptional communication skills, able to explain complex technical concepts clearly to cross-functional partners 

  • Broad understanding of core machine learning concepts and their application in large-scale, real-world machine-learning systems

  • Familiarity end-to-end machine learning pipelines (e.g. training or production deployment) and common challenges like explainability.

Generally, 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 $466,000.00 - $750,000.00. This compensation range will vary based on location.

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 details about our Benefits here.

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

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

About Netflix

Netflix, Inc. is an American media company founded on August 29, 1997 by Reed Hastings and Marc Randolph in Scotts Valley, California, and currently based in Los Gatos, California, with production offices and stages at the Los Angeles-based Hollywood studios (formerly old Warner Brothers studios) and the Albuquerque Studios (formerly ABQ studios). It operates an eponymous over-the-top subscription video on-demand service, which showcases acquired and original programming as well as third-party content licensed from other production companies and distributors. Netflix is also the first streaming media company to be a member of the Motion Picture Association.
Learn more about Netflix
Size
11,300 employees
Market Cap
$127.6 billion
Industry
Net Income
$2.7 billion
Founded
1997
5 Year Trend
+27.5%
Revenue
$24.9 billion
NASDAQ

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