Research Scientist Intern, Ads Quality (PhD)

Meta•Menlo Park, CA

2d•$7,650 - $12,667

About The Position

Meta is seeking a Research Scientist Intern for the Ads Quality team within Facebook Ads Recommendations. Ads Quality aims to deliver long-term value for people and businesses by showing ads people find valuable. The team determines which ads to show, how many, and where, balancing advertiser value with the user experience. The intern will frame practical, high-impact problems in ads recommendation as rigorous machine-learning problems and develop solutions with guidance from research scientists and software engineers. The work focuses on modeling the long-term consequences of ads: how impressions and engagements affect future user behavior and value. This is challenging because long-term outcomes are sparse and delayed, counterfactual value is unobserved, user actions cannot be randomized, and ads interact with organic content. The role combines causal inference, sequence modeling, and reinforcement learning to improve recommendation systems at the scale of billions of people. Internships are 12–16 or 24 weeks long, with various start dates throughout the year.

Requirements

Currently have or be in the process of obtaining a Ph.D. in Computer Science, Machine Learning, Statistics, Operations Research, Economics, or a relevant technical field.
Obtain work authorization in the country of employment by the time of hire and maintain authorization throughout employment.
Have experience with Python, C++, C, Java, or another related programming language.
Have experience with deep-learning frameworks such as PyTorch or TensorFlow.
Have experience building systems based on machine-learning or deep-learning methods.
Have research experience in one or more of causal inference, recommender systems, reinforcement learning, or sequence modeling.

Nice To Haves

Intent to return to the degree program after completing the internship or co-op.
A record of research achievements, demonstrated through grants, fellowships, patents, or publications at leading conferences such as NeurIPS, ICML, ICLR, KDD, RecSys, WWW, or WSDM.
Research or project experience in reinforcement learning, including offline RL, contextual bandits, off-policy evaluation, value-function learning, or temporal-difference learning, and its application to recommendation or advertising.
Experience with causal inference for decision-making, such as uplift modeling, heterogeneous treatment effects, doubly robust estimation, instrumental variables, or regression discontinuity.
Experience with large-scale sequence models or generative recommenders, such as Transformer-based user sequence models.
Demonstrated software-development experience through technology internships, work experience, coding competitions, or widely used contributions to open-source machine-learning repositories.
Experience working and communicating cross-functionally in a fast-paced team environment, with the ability to identify research opportunities in real-world applications, select appropriate machine-learning methods, and proactively drive iterations based on analysis of results.

Responsibilities

Perform research to advance long-term value modeling in large-scale recommendation systems.
Develop novel causal and sequential machine-learning methods, such as reward design and reinforcement-learning models, to estimate the long-term effects of ads on people's future engagement and value.
Design offline evaluations and online experiments to test whether learned long-term value signals improve outcomes for people and advertisers.
Analyze and improve the efficiency, scalability, and stability of deployed ranking systems.
Collaborate with researchers and cross-functional partners, communicating research plans, progress, and results.
Publish research results and contribute to research applicable to Meta product development.