Machine Learning Engineer 5 - Ads Platform Engineering
Netflix · Los Gatos, United States · 5mo ago
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.
We launched a new ad-supported tier in November 2022 to offer our members more choice in how they consume their content. Our new tier allows us to attract new members at a lower price point, while also creating a compelling path for advertisers to reach audiences that are deeply engaged.
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 etc. 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.
Open Roles
We are hiring for multiple roles across our Ads Platform teams. As you progress through the interview process, you will be assessed for the following roles:
The Ads Inventory Management & Forecasting team builds state-of-art realtime inventory forecasting solution leveraging ML models and high performance ad server simulations. The team also builds systems that enable publisher inventory management solutions, which supports various monetization strategies such as dynamic pricing, rate card management, product packaging, inventory split and yield optimization.
The Core Ads Serving team powers real-time ad decisioning, delivering relevant, high-quality ads while balancing revenue goals and advertiser outcomes. They build complex ML models for low-latency environments and manage core systems that enhance campaign performance through budgeting, pacing algorithms, and dynamic allocation across direct and programmatic. Additionally, the team develops models for goal-based delivery optimization, such as CPC, CPV, and CPCV.
The Ads Programmatic team builds interfaces with selected SSPs and DSPs to integrate with Advertisers' primary buying mechanisms to unlock spend.
The Ads Member Experience team is responsible for building and serving the different ad formats available on the platform. The team owns the integration between the different Netflix clients (TV, mobile app, web) and the ads serving infrastructure. One of its primary goals is to optimize how different ad formats are integrated with the Netflix member experience.
The Ads Identity & Audiences team is revolutionizing ad experiences by utilizing advanced machine learning models for identity resolution and precise behavioral and contextual audience targeting. We create foundational systems that deliver relevant and engaging ads to Netflix members, all while upholding their privacy. Our continuous refinement of models generates a flywheel effect, enhancing member experiences and driving optimal advertiser outcomes at scale.
Skills & experience we’re seeking:
Proficiency in Java, C++, Python, or Scala with a solid understanding of multi-threading and memory management
Experience in building end-to-end ML model deployment and inference infra for low-latency real-time ad systems.
Experience in handling data at extremely large volumes with big data tools like Spark.
Yield Optimization, scoring, and bid ranking models, and Dynamic Allocation of direct/programmatic guaranteed and non-guaranteed inventory
Modeling and Building Cost Per Click, Cost Per View, and Cost Per Video Complete modeling and optimization
Productionized predictive models to forecast the effectiveness of advertising campaigns, including metrics like impressions, reach, clicks, conversions, and ROI.
Building Scalable Simulation solution to model different inventory scenarios, including demand fluctuations, pricing strategies, and inventory allocation.
General understanding of the advertising marketplace and landscape, with a focus on publisher side challenges like optimizing fill rates and maximizing revenue in the context of inventory management.
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:
Experience in productionizing ML models and deploying models at scale.
Contributed to an ads industry technology standard (e.g VAST, OpenRTB) or worked on an industry consortium effort, working group etc.
Familiar with publisher-side ad tech systems including ad servers, bidders, yield optimizers, and their demand-side counterparts (SSPs/DSPs)
Good understanding of Lucene index and had experience building Lucene index with large volume of data.
Familiarity with legal compliance and changing landscape of ads regulations around the world.
Experience working in the CTV space and knowledge of its unique constraints
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.
Inclusionis 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.