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Meta · 1 day ago

AI Research Scientist, CoreML - Monetization

New York, NY

Full-time

Onsite

Mid, Senior Level

$184K/yr - $257K/yr

3+ years exp

Meta is a technology company that builds tools to help people connect and grow businesses. They are seeking an AI Research Scientist to drive innovative research initiatives in monetization strategies, focusing on developing advanced recommender systems and shaping the future of technology at Meta.

Computer Software

Comp. & Benefits

Responsibilities

Extracting meaningful signals from both 1st-party and 3rd-party data sources

Advancing representation learning

Scaling solutions to efficiently process hundreds of billions of data points

Driving continuous algorithmic innovation

Seamlessly productionizing research breakthroughs all while optimizing serving costs

Qualification

Machine LearningNatural Language ProcessingPythonPyTorchResearch PublicationsCausal LearningSequence LearningTechnical LeadershipCollaborationProblem Solving

Required

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

PhD in Computer Science, Machine Learning, or a relevant technical field

3+ years of industry research experience in LLM/NLP, computer vision, or related AI/ML model training

Experience as a technical lead on a team and/or leading complex technical projects from end-to-end

Publications at peer-reviewed conferences (e.g. ICLR, NeurIPS, ICML, KDD, CVPR, ICCV, ACL)

Programming experience in Python and hands-on experience with frameworks such as PyTorch

Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

Preferred

A track record of impactful research in the ranking/retrieval/recommendation space, as demonstrated by publications, open-source contributions, or real-world deployments

First-authored publications at peer-reviewed conferences (e.g. ICLR, NeurIPS, ICML, KDD, CVPR, ICCV, ACL)

Experience in pre-training, post-training, fine-tuning models

Experience in causal learning, sequence learning, classification, neural networks, graph learning, items associated, in-depth content understanding (user behavior, user interaction)

Experience solving complex problems and comparing alternative solutions, tradeoffs, and broad points of view to determine a path forward

Willing to collaborate with others in a productive, interdisciplinary environment