Ads Recommendation Expert

About Huawei

Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. We are committed to bringing digital to every person, home and organization for a fully connected, intelligent world.At Huawei, we have two key drivers of innovation: science and technology, and customer needs. Both commercial value and market demands are driving our innovation and determining how we invest in science and technology. Breakthroughs in technology, in return, stimulate customer needs and allow us to create greater value for customers.

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 Huawei Petal Ads

Petal Ads is a smart marketing platform for Huawei devices. It provides marketing and traffic monetization services for advertisers and publishers worldwide to help them achieve business growth and improve brand value. It does this by leveraging Huawei's "1+8+N" all-scenario ecosystem, Huawei apps' marketing capabilities, massive premium third-party traffic, and powerful ad technologies. By June/2023, Petal Ads has cooperated with advertisers spanning 200+ industries, while more than 60,000 apps worldwide have integrated Ads Kit.

About the Job

As an Ads Recommendation Expert at Huawei Ads, you will play a pivotal role in shaping the next generation of large-scale recommendation and advertising systems. You will join a world-class team of scientists and engineers to tackle some of the most complex problems in computational advertising—ranging from personalization and ranking to reinforcement learning and multi-modal recommendations.Unlike a purely applied role, this position requires a seasoned domain expert with both deep hands-on experience in recommender systems and strategic vision to design, guide, and advance Huawei's global ads recommendation pipeline. You will drive innovation that enhances user engagement, advertiser ROI, and long-term ecosystem growth across billions of impressions.This is a unique opportunity to influence Huawei Ads' global recommendation strategy, collaborate across research and product teams worldwide, and ensure our systems remain at the cutting edge of recommender systems science and industrial practice.

Responsibilities

• Ensure advancement and effectiveness of large-scale advertising recommendation model technology, including but not limited to 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 (preferred) or Master's 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.
• (Preferred) Contributions to the research community (e.g., publications in RecSys, KDD, WWW, WSDM, SIGIR) or recognized innovation in large-scale recommendation products.

Key skills/competency

• Recommendation Systems
• Machine Learning
• Computational Advertising
• Deep Learning
• Reinforcement Learning
• Transformers
• GNNs
• A/B Testing
• Large-Scale Systems
• Strategic Vision