Expert in Personalized Advertising Recommendations

19 hours ago

Dublin, Dublin City, Ireland beBeeRecommendation Full time €90,000 - €115,000

Job Opportunity:

We are seeking a seasoned professional to join our team as an Ads Recommendation Expert. This is an exciting opportunity for a talented individual with expertise in recommender systems to shape the next generation of large-scale recommendation and advertising systems.

About the Role:

  • Ensure the advancement and effectiveness of large-scale advertising recommendation model technology, including LLM applications in ad recommendations, massive parameter models, real-time incremental updates, and sparse scenario predictions.
  • Own and deliver application impact of core ad recommendation models (pCTR, pCVR, acceptance rate, etc.), continuously driving improvements to optimize user experience and advertiser ROI.
  • Lead innovation in recommendation algorithms: Design, develop, and deploy next-generation recommendation systems for ads, with a focus on personalization, contextual relevance, fairness, and long-term value optimization.
  • Drive strategic roadmap: Define and guide the evolution of Huawei's ads recommendation pipeline, from retrieval to ranking to post-auction optimization, leveraging state-of-the-art techniques (transformers, GNNs, reinforcement learning, foundation models for recsys).
  • Solve global-scale challenges: Partner with international research labs, product, and engineering teams to address technical bottlenecks in large-scale recommendation systems, ensuring solutions are robust, scalable, and aligned with business goals.
  • Promote cross-team collaboration and knowledge sharing: Act as a thought leader across Huawei's global ecosystem (HQ, AALA, recommendation/search/cloud groups), disseminating best practices and mentoring teams to elevate overall recsys capability.
  • Conduct and oversee experiments: Lead the design of A/B tests and statistical evaluations to measure algorithm effectiveness and business impact, ensuring high scientific rigor.
  • Advance frontier research: Stay at the forefront of recommender system research (e.g., self-supervised learning, retrieval-augmented recsys, privacy-preserving federated learning) and translate academic insights into production-ready innovations.

Requirements:

  • PhD or Master's degree in Computer Science, Information Systems, Statistics, Mathematics, or a related quantitative field.
  • 6+ years of experience delivering large-scale machine learning solutions, with a proven track record of building and deploying recommender systems in advertising or large-scale platforms.
  • Recognized expertise in recommender systems: collaborative filtering, matrix factorization, user-ad matching, deep learning (transformers, GNNs), reinforcement learning, or bandit algorithms.
  • Strong understanding of ad-relevant recommendation challenges: personalization under constraints, auction-aware recsys, multi-objective optimization (RPM, eCPM, engagement, retention).
  • Hands-on experience with large-scale ML and recommender frameworks (TensorFlow Recommenders, PyTorch, etc.), and production-level pipeline design.
  • Demonstrated ability to think strategically about recommendation system architectures and their impact on business and ecosystem growth.
  • Proven collaboration in cross-functional and global teams, with the ability to influence and align stakeholders across research, engineering, and product domains.
  • Excellent communication skills for presenting technical vision and complex solutions to both expert and non-expert audiences.

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