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Netflix

ML Engineer L5 - Ads Platform Engineering (Forecasting)

Posted 5 Days Ago
Be an Early Applicant
Remote
Hiring Remotely in USA
100K-720K
Senior level
Remote
Hiring Remotely in USA
100K-720K
Senior level
As an ML Engineer, you will build and productionize predictive models for ad inventory and campaign performance, contributing to the growth of Netflix's ad platform.
The summary above was generated by AI

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 one of the world's leading entertainment services with over 260 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.

In April 2022, we introduced a new, more affordable, ad-supported tier for our customers, marking a significant milestone for Netflix. Our focus is now on expanding choices for consumers and providing advertisers with a premium TV brand experience that surpasses traditional linear formats. The challenge ahead involves scaling our ad tech to maximize its impact on our business.

Our Team

The Ads Platform Engineering teams build advertising systems and integrations that powers the delivery of ads using our world-class content delivery ecosystem. We use a number of Netflix investments and innovations to power our ads - unique mix of client and server-side ad insertions, state of the art content delivery system, ad encoding recipes, content understanding and metadata. We deliver ads in a manner that’s thoughtful of our member’s viewing experience and drive great outcomes for advertisers. We also ensure that advertiser brand safety is ensured during serving, members only see the most appropriate ads for them.

Our team is new and yet faced with the enormous ambitions of building highly performant advertising systems and delivering high impact to our business by monetizing our incredible slate of content. As one of the newest entrants in the Connected TV advertising space that’s rapidly growing, we seek to build unique value propositions that help us differentiate from the competition and become a market leader in record time.

We are looking for highly motivated engineers working in the advertising space who are excited to join us on this journey.

Experience & Skills:

We are seeking an ML engineer with experience in the following areas: 

  • Productionize models to forecast ad inventory availability and performance. This includes predicting future inventory needs based on historical data, market trends, and seasonal variations.

  • Productionized predictive models to forecast the effectiveness of advertising campaigns, including metrics like clicks, impressions, conversions, and ROI.

  • Bringing complex models to life in low-latency real-time advertising environments

  • Building Scalable Simulation solution to model different inventory scenarios, including demand fluctuations, pricing strategies, and inventory allocation.

  • Budgeting and Pacing systems, models, and algorithms

  • Applying Modeling and machine learning techniques for business problems at the intersection of product, data science, and engineering

  • Collaborate with cross-functional stakeholders from science team, product, engineering, operations, design, consumer research, etc., to productionize and deploy models at scale

Nice to Haves:

  • Publisher-side ad tech systems including ad servers, bidders, yield optimizers, and their demand-side counterparts (SSPs/DSPs)

  • Yield Optimization, scoring, and bid ranking models and Dynamic Allocation of direct/programmatic guaranteed and non-guaranteed inventory

  • Contributed to an ads industry technology standard (e.g  VAST, OpenRTB) or worked on an industry consortium effort, or working group.

  • Familiarity with legal compliance and the changing landscape of ad regulations around the world.

  • Experience working in the CTV space and knowledge of its unique constraints

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 $100,000 - $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.

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

Top Skills

Ad Tech Systems
Algorithms
Ctv
Ml
Predictive Modeling

Netflix Los Angeles, California, USA Office

5808 W Sunset Blvd, Los Angeles, CA, United States, 90028

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