AI Research Scientist, CoreML - Monetization
Immigration summary
Visa sponsorship
This employer sponsors this kind of work repeatedly, and is still filing this year.
5,686 recent H-1B filings · 572 similar-role filings
View visa evidenceGreen card sponsorship
This employer has recently sponsored green cards at scale.
742 recent certified PERM filings · 107 similar-role filings
View green card evidenceJob 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 all 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
Sponsorship evidence
Why Openbound reached the conclusions above.
Visa sponsorship evidence
Current posting
Silent on sponsorship
Employer H-1B history
- 5,686
- recent certified H-1B filings
- 3,007
- new-hire petitions
- 572
- filings for similar roles
- 5,150
- so far in FY2026
Filed titles like this role: research scientist · ai research scientist · ux research scientist · al research scientist
More evidence details
- 572 certified H-1B filings for this same role, 3,007 new-hire petitions across the employer, and 2,752 new hires already this year
- 572 certified H-1B filings for this same role
- 5,686 recent certified H-1B filings across the employer
- Still filing this year — 5,150 filings in FY2026
- 2,778 USCIS H-1B new-employment approvals, counted separately from LCA filings
- Strong filing activity in CA
- 11,582 further USCIS approvals for extensions or transfers
- The posting says nothing about sponsorship either way
Strong filing activity in CA.
Green card sponsorship evidence
Employer PERM history
- 742
- recent certified PERM filings
- 107
- filings for similar roles
- 478
- filings in this location
Filing history reflects past employer behavior; it isn't a promise for this opening.
All open roles at MetaHow Openbound evaluates sponsorship
Visa history uses official U.S. Department of Labor H-1B LCA disclosure data and USCIS H-1B petition history. Green card history uses DOL PERM disclosure data. Each is read for the employer as a whole, for roles like this one, and for this location, weighted toward the most recent fiscal years.
An employer is matched to its filing entities by verified legal name and reviewed aliases; a match is never made on a name resemblance alone. Where no verified entity can be matched, the page says so and draws no conclusion from the absence. 1,277 filing titles were examined for this employer.
What this posting states outranks history in both directions, and an employer's published policy outranks past filings. Filing history reflects past behavior; it is not a promise of sponsorship for this opening, and none of this is legal advice.