Netflix · 1 week ago
Machine Learning Engineer 5 - Ads Platform Engineering
United States
Full-time
Remote
Mid, Senior Level
$100K/yr - $720K/yrNetflix is one of the world's leading entertainment services, and they are seeking a Senior Machine Learning Engineer to join their Ads Platform Engineering team. The role involves building and optimizing machine learning models and systems to enhance ad delivery and improve member experience while driving advertiser outcomes.
Digital EntertainmentMedia and EntertainmentTVVideo Streaming
Responsibilities
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
Qualification
Required
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
Preferred
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
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
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
Company
Netflix
Netflix is an online streaming platform that enables users to watch TV shows and movies.
Founded in 1997
10001+ employees
H1B Sponsorship
Netflix has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (310)
2024 (309)
2023 (191)
2022 (261)
2021 (268)
2020 (225)
Funding
Current Stage
Public CompanyTotal Funding
$63.91BKey Investors
Wells FargoTCVGroupe Arnault
2025-12-05Post Ipo Debt· $59B
2024-08-01Post Ipo Debt· $1.8B
2018-05-05Post Ipo Debt· $2.67M
Leadership Team
Gregory Peters
Co-CEO
Ted Sarandos
Co-CEO
Recent News
2026-01-16
2026-01-16
2026-01-16
Company data provided by crunchbase