Location: Dublin, Ireland
About Huawei
Huawei’s products and services are available in more than 170 countries and are used by a third of the world’s population. Huawei Consumer Business Group (CBG) is one of Huawei’s three business units and covers smartphones, PCs and tablets, wearables and cloud services, etc. Huawei Mobile Services (HMS) is part of CBG and develops new cloud services offered free of charge to Huawei mobile device users.
HMS ecosystem is now the third largest ecosystem in the world with more than 96,000 global apps integrated with HMS Core. HMS Apps continues to launch globally, with content apps such as HUAWEI Music, HUAWEI Video, HUAWEI Themes, HUAWEI Reader and HUAWEI Game Center taking centre stage in various countries and regions.
About the IRC
Huawei Ireland Research Centre's (IRC) mission is to position Huawei as a recognized technology leader and global information and communications technology (ICT) solutions provider. To achieve this we are building an industry-recognized multi-discipline Research Centre of experts focusing on medium-term to long-term issues.
The IRC will work closely with an open innovative ecosystem with Huawei customers to address real-world issues. The IRC will also engage with key European universities to build a basic research capability to support Huawei technical projects.
About the Job
As a Personalization and Recommendation Research Expert at Huawei Ireland Research Centre, you will help shape the next generation of large-scale personalization and recommendation systems.
The role sits at the intersection of recommender systems research, large-scale sequential modeling, and industrial personalization. You will work on systems that model user behavior across rich interaction streams, learn robust item and event representations, and support high-quality personalized experiences across different domains and product scenarios.
A central part of the role will be to advance Huawei’s roadmap in generative recommendation and next-generation personalization. This includes semantic ID representations, transformer-based sequential recommendation, efficient attention for long sequences, unified recall and ranking architectures, and principled evaluation of large-scale recommendation models.
We are looking for a senior researcher or technical leader who can combine hands-on modeling experience with strategic judgment. The successful candidate will help define the technical roadmap, guide research directions, mentor team members, and translate promising ideas into production-relevant systems.
Responsibilities
· Define and drive research directions for next-generation personalization and recommendation systems, with a focus on generative recommendation, large-scale sequential modeling, and unified recall and ranking.
· Design, develop, and evaluate generative recommendation models that treat recommendation as sequence modeling over user events, items, actions, or semantic identifiers.
· Develop and evaluate semantic ID representations, including hierarchical, non-hierarchical, graph-informed, and learned tokenization approaches.
Investigate long-sequence recommendation models capable of using rich user histories and device event streams.
· Explore efficient attention mechanisms and scalable transformer architectures for long-context recommendation.
· Study scaling behavior in recommendation models, including the relationship between model size, data size, sequence length, and downstream performance.
· Translate research ideas into production-relevant models for large-scale personalization and recommendation systems.
· Design rigorous offline and online evaluations, including ranking metrics, retrieval metrics, calibration, latency, throughput, robustness, and business impact.
· Collaborate with research, engineering, product, HQ, and international teams to ensure solutions are scalable, robust, and aligned with product objectives.
· Mentor researchers and engineers, promote technical best practices, and contribute to publications, patents, technical reports, and external research engagement where appropriate.
Requirements
· PhD preferred, or Master's degree with strong research and industrial experience, in Computer Science, Mathematics, Statistics, Machine Learning, or a related quantitative field.
· 6 or more years of experience in machine learning, recommender systems, search, ranking, personalization, or large-scale user behavior modeling.
· Strong hands-on experience in one or more of the following areas: sequential recommendation, retrieval, ranking, user behavior modeling, transformer-based recommendation, multi-task learning, multi-objective optimization, reinforcement learning, bandits, multi-modal recommendation, or cross-domain recommendation.
· Strong understanding of modern recommendation architectures, including deep retrieval, ranking models, two-tower models, sequence models, transformers, GNNs, and representation learning.
Experience designing, training, evaluating, and debugging large-scale ML models in production or production-adjacent environments.
· Strong knowledge of recommendation evaluation, including offline metrics, online experimentation, A/B testing, calibration, statistical significance, and business metric alignment.
· Ability to connect research questions to practical impact in complex industrial systems.
· Experience with large-scale ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent systems.
· Strong communication skills and demonstrated ability to work across research, engineering, and product teams.
Preferred Qualifications
· Research or industrial experience in generative recommendation, semantic IDs, long-sequence modeling, efficient attention, or foundation models for recommendation.
· Experience building unified recommendation architectures across retrieval and ranking.
· Experience with personalization systems across multiple domains, modalities, or product surfaces.
· Experience studying scaling laws, model capacity, sequence length, or data scaling in recommendation systems.
· Publications in leading venues such as RecSys, KDD, WWW, WSDM, SIGIR, ICML, NeurIPS, ICLR, or related conferences.
· Experience mentoring researchers or leading small technical teams.
What We Offer
· The opportunity to work on strategic recommender systems research with direct relevance to Huawei's global products and ecosystem.
· A role with both research depth and practical impact, connecting frontier recommender systems ideas to large-scale industrial systems.
· A collaborative research environment involving scientists, engineers, product teams, and academic partners.
· The opportunity to help define the technical direction of generative recommendation and next-generation personalization at Huawei Ireland Research Centre.
Check out Life at Huawei Ireland Research Centre:
https://www.youtube.com/watch?v=3gR64sYSnOA&feature=youtu.be
Due to the high volume of replies, only candidates shortlisted for interview will be contacted.
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