Research Scientist Intern, FM Quality ML (PhD)

Meta was built to help people connect and share, and over the last decade our tools have played a critical part in changing how people around the world communicate with one another. With over a billion people using the service and more than fifty offices around the globe, a career at Meta offers countless ways to make an impact in a fast growing organization.

Meta is seeking Research Interns to join the FM Quality ML team within Facebook Ads Ecosystem. FM Quality sits at the intersection of machine learning and system optimization across Meta's family of apps. We answer three fundamental questions: how many ads to show, which ads are best for each person, and how to deliver them, balancing revenue growth with user experience. Our team applies state-of-the-art ML, Generative AI, Reinforcement Learning, and large-scale systems to personalize ad quality across Feed, Videos, Reels, and Stories. We own 0→1 initiatives end-to-end and collaborate broadly across Facebook and Instagram to build the foundation for sustainable business growth. Our interns will have the opportunity to frame practical, high-impact ads quality problems as rigorous machine learning problems, and bring their own expertise to build innovative solutions under the guidance of our research scientists and software engineers.

The central research challenge is to build a quality system that understands each person and each ad deeply enough to reason about long-term user value, and to show people the ads they will like and trust. People tell us about their ad experience through many quality signals, such as survey ratings, hiding or reporting an ad, and their experience after a click. Each signal captures only part of the picture, and their effects add up across a session and over weeks. We are post-training large language models into a quality foundation model that reasons over a person's ad and organic interaction history together with the ad's content, and returns both an explanation grounded in that history and calibrated predictions for many quality signals at once. Open problems include post-training recipes and reward design that make reasoning measurably improve prediction and calibration, representing ads and user behavior in a shared semantic vocabulary the model can reason over, evaluating reasoning against people's own judgments, and distilling the model's reasoning into efficient production models that serve within strict latency and compute constraints. Offline evaluation and online experiments help us connect model improvements to better ad experiences and long-term value for people and advertisers.

If you are excited about using LLM reasoning to help billions of people see ads they like and trust, come join us!

Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

Responsibilities

Post-train large language models with supervised fine-tuning and reinforcement learning to reason over user behavior history and ad content, and predict multiple ad quality signals at once.
Design reasoning formats, model inputs, and reward functions so that reasoning measurably improves the accuracy and calibration of quality predictions.
Develop evaluation methods for LLM reasoning and predictions, including human-anchored rubric-graded benchmarks, LLM-as-a-judge, groundedness checks, and comparison against production models.
Distill LLM reasoning and predictions into efficient production models, and validate gains through offline metrics and online experiments.
Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
Publish research results and contribute to research that can be applied to Meta product development.

Qualifications

Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, Information Retrieval, or relevant technical field
Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment
Experience with Python, C++, C, Java or other related language
Experience with deep learning frameworks such as PyTorch or TensorFlow
Experience building systems based on machine learning, deep learning methods, information retrieval, or natural language processing
Familiarity with algorithms behind generative model training and common deep learning architectures (Transformers, GPTs, BERT etc.) Intent to return to the degree program after the completion of the internship/co-op
Proven track record of solid research achievements as demonstrated by grants, fellowships, patents, as well as publications at leading conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, KDD, SIGIR, WSDM, WWW, RecSys, or CIKM
Prior research or project experience in one or more of the following areas: post-training of large language models (supervised fine-tuning, reinforcement learning, preference optimization), reasoning and chain-of-thought methods, reward design, LLM evaluation (e.g., rubric-based or LLM-as-a-judge), uncertainty estimation and calibration, knowledge distillation, sequence modeling, learned content identifiers, multimodal understanding, personalized recommendation, long-term user behavior modeling, and their applications in recommendation or ads systems
Demonstrated software development experience via tech internships, work experience, coding competitions, or widely used contributions in open source machine-learning repositories
Experience working and communicating cross functionally in a fast-paced team environment. Ideal candidates should have the ability to quickly understand and identify the research opportunities behind real-world applications, select the appropriate ML methods to explore, and proactively drive the iterations based on clear analysis of the current results