AI Research Scientist, CoreML - Monetization

Meta; Bellevue (WA); On-site; USD 184,000 - 257,000; Full time

Benefits offered by this job

Bonus

Equity

Comprehensive benefits

Job summary

Meta is seeking a Research Scientist in Bellevue, WA, to innovate on cutting-edge personalized ad technologies. You will extract signals, advance representation learning, and lead technical projects in AI/ML. Ideal candidates have a PhD and 3+ years in LLM/NLP, proven project leadership experience, and must hold relevant publications. The role offers a competitive salary ranging from $184,000 to $257,000 a year plus bonuses and benefits, highlighting Meta's commitment to diversity and equal opportunity.

Qualifications

• 3+ years of industry research experience in LLM/NLP or related AI/ML.
• Experience leading complex technical projects end-to-end.
• Must obtain work authorization in the country of employment.

Responsibilities

• Extract meaningful signals from various data sources.
• Advance representation learning and drive algorithmic innovation.
• Productionize research breakthroughs while optimizing costs.

Skills

Research experience in LLM/NLP

Programming experience in Python

Experience with PyTorch

Technical lead experience

Publications at peer-reviewed conferences

Education

Bachelor’s degree in Computer Science or related field

PhD in Computer Science or Machine Learning

Job description

Meta’s Monetization pillar is at the cutting edge of delivering highly personalized ads that create maximum value for both users and advertisers. Within this pillar, the Ranking & AI (RAI) Research team drives state‑of‑the‑art research initiatives, focusing on high‑impact, high‑risk projects—true moonshots—with the potential to redefine Meta’s monetization strategies. By consistently pushing the boundaries of what’s possible, we deliver breakthrough innovations that not only advance Meta’s business objectives but also result in publications at top‑tier conferences. Inspired by recent breakthroughs in large language models (LLMs), the RAI Sequence Learning team is pioneering a transformative approach to recommender systems. We are reimagining recommendation as a generative sequence‑modeling problem, moving beyond traditional methods that treat recommendations as classification tasks on pairs. Instead, our approach models user and ad content, as well as historical interaction data, as sequences—unlocking new possibilities for personalization and relevance. As a research scientist on this team, you will play a pivotal role in shaping the future of technology and business at Meta, especially as we enter the era of artificial general intelligence (AGI). Your contributions will directly influence the trajectory of Meta’s monetization strategies and help define the next generation of recommender systems.

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 while optimizing serving costs

Minimum Qualifications

• 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 Qualifications

• 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

Benefits & Compensation

$184,000/year to $257,000/year + bonus + equity + benefits. Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.

Meta is proud to be an Equal Employment Opportunity and Aff…

We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.

Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at [e-mail].