Machine Learning Scientist (L5) - Core Ads Algorithms

Netflix

About The Position

Netflix is one of the world's leading entertainment services, with over 300 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. We launched a new ad-supported tier in November 2022 and are building an in-house world-class ad tech ecosystem to offer our members more choices in consuming 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 deeply engaged audiences. The Core Ads Algorithms team is the engine behind the intelligence that powers ad personalization at Netflix. Our mission is to develop innovative, data-driven solutions that deliver highly relevant ad experiences to our members and drive impactful results for advertisers—all while maintaining the exceptional quality and personalization that define the Netflix experience.

Requirements

Advanced degree (PhD or Master's) in Operations Research, Applied Mathematics, Computer Science, or related quantitative field
Deep expertise in applying linear programming and optimization techniques to business problems
Proficiency with optimization software (Gurobi, CPLEX, FICO Xpress, OR-Tools) and Python, R, or similar languages for data analysis and optimization modeling
Strong business acumen with ability to translate business requirements into mathematical constraints
Experience working with large-scale data systems and production ML pipelines
Understanding of A/B testing methodologies and statistical analysis

Nice To Haves

Proficiency in distributed systems programming (preferably Python or Scala)
Experience with large-scale optimization problems and dual decomposition techniques
Knowledge of first-order optimization methods (gradient descent variants, proximal methods)
Experience building production optimization systems handling a large number of variables
Strong mathematical background in linear algebra, convex analysis, and optimization theory

Responsibilities

Formulate complex business problems in budget allocation, marketplace matching as multi-objective optimization problems
Design and implement scalable optimization algorithms for marketplace applications that balance competing business objectives (revenue, advertiser outcome, user satisfaction)
Integrate machine learning model predictions with optimization formulations for personalized decision-making
A/B test and monitor performance for optimization-driven features
Translate complex model outputs into actionable metrics and KPIs for non-technical stakeholders, ensuring clarity and business alignment.
Serve as a strategic thought partner to product managers and business stakeholders, directly influencing product direction and improving user experience.
Effectively communicate findings and insights to both technical and non-technical audiences, driving adoption and understanding of ML-driven solutions.