Research Scientist, Machine Learning

Meta•Menlo Park, CA

4d•$154,000 - $217,000

About The Position

Meta is seeking a Research Scientist to advance the state of the art in ads AI research. The role conducts original research in areas such as operations research, optimization, representation learning, time series forecasting, and generative modeling, with the goal of driving fundamental breakthroughs for Meta's AI-driven ads products and beyond. The scientist works with researchers and engineers to translate novel findings into real-world impact at unprecedented scale. The position includes designing and executing research projects, developing and validating algorithms through rigorous experimentation, and translating findings into scalable implementations for production AI systems. The scientist also defines evaluation frameworks, collaborates across engineering, product, and data science, communicates research findings, mentors colleagues, drives technical roadmaps, and contributes to the research community.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
At least 2 years of experience conducting machine learning research, including developing and evaluating novel models, algorithms, or training methodologies.
Experience publishing original research in peer-reviewed machine learning venues such as NeurIPS, ICML, ICLR, CVPR, ACL, or equivalent.
Experience implementing and training large-scale machine learning models using frameworks such as PyTorch or TensorFlow.
Experience designing experiments, selecting appropriate metrics, and drawing statistically sound conclusions from empirical results.
Experience communicating complex research findings in writing to technical and non-technical stakeholders through papers, design documents, or technical reports.
PhD in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, or a relevant technical field.

Nice To Haves

Research experience in one or more specialized areas such as operations research, optimization, forecasting, multimodal learning, reinforcement learning, or generative modeling.
Demonstrated ability to identify high-impact research directions aligned with long-term product or platform goals.
Experience mentoring other researchers and contributing to the technical growth of a research organization.
Track record of driving research from initial hypothesis through production deployment at scale, including collaboration with applied engineering teams.

Responsibilities

Design and execute original AI research projects spanning areas such as deep learning architectures, optimization methods, and generative models.
Design optimization and forecasting methods for allocation planning and marketplace optimization.
Develop and validate novel algorithms and modeling approaches through rigorous experimentation, ablation studies, and statistical analysis.
Translate research findings into scalable implementations that can be integrated into production AI systems serving billions of users.
Define and instrument evaluation frameworks, benchmarks, and metrics to measure research progress and model quality.
Collaborate with cross-functional partners across engineering, product, and data science to align research directions with high-impact product opportunities.
Communicate research findings through internal technical reports, external publications, and presentations to technical and non-technical audiences.
Mentor other researchers and engineers on the team, providing technical guidance and constructive feedback on research methodology and implementation.
Drive the technical roadmap for one or more research areas by identifying opportunities, building stakeholder alignment, and coordinating execution across teams.
Contribute to the broader research community through peer-reviewed publications, open-source contributions, and participation in academic and industry venues.