Machine Learning Engineer - Ads Platform Engineering

April 24

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Netflix

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B2C • eCommerce • Media

Netflix is a global streaming service that offers a wide variety of award-winning television shows, movies, anime, documentaries, and more on thousands of internet-connected devices. It allows users to watch instantly and provides original content produced by Netflix itself, catering to diverse tastes. As a pioneer in original programming since its first series in 2013, Netflix aims to entertain audiences worldwide through immersive storytelling and innovative technology.

- employees

Founded 1997

👥 B2C

🛍️ eCommerce

📱 Media

💰 $20M Post-IPO Equity on 2022-01

📋 Description

• Netflix is seeking a Machine Learning Engineer to work on its Ads Platform Engineering team • The role involves building advertising systems and integrations for ad delivery using cutting-edge technology • Responsibilities include developing ML models, optimizing ad performance, and ensuring brand safety during ad serving • Candidates will work in a highly dynamic team and face ambitious goals in the rapidly growing connected TV advertising space

🎯 Requirements

• 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

🏖️ Benefits

• Health Plans • Mental Health support • 401(k) Retirement Plan with employer match • Stock Option Program • Disability Programs • Health Savings and Flexible Spending Accounts • Family-forming benefits • Life and Serious Injury Benefits • Paid leave of absence programs • 35 days annually for paid time off for full-time hourly employees • Flexible time off for full-time salaried employees