About this role
Summary: Build scalable data and ML infrastructure for Huawei Petal Ads' recommendation and auction systems. Collaborate with data scientists to operationalize advanced ML models, ensuring efficient feature engineering, robust training pipelines, and reliable production deployment.
*Location*: Dublin, Ireland
### *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 turn, 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 now, Petal Ads has cooperated with advertisers spanning 200+ industries, while more than 60,000 apps with more than 730 million monthly active users worldwide have integrated Ads Kit.
### *About the Job*
As an Ads Recommendation Machine Learning Engineer, you will play a key role in building scalable data and ML infrastructure that powers Huawei Petal Ads’ recommendation and auction systems. You will work closely with data scientists and domain experts to operationalize advanced ML models, ensuring smooth data preparation, efficient feature engineering, robust training pipelines, and reliable deployment into production.
This role is ideal for engineers passionate about applying machine learning at scale, optimizing model performance, and enabling rapid experimentation in one of the world’s largest advertising ecosystems.
### *Responsibilities*
- Collaborate with data scientists to productionize ML models for recommender systems and auction mechanisms.
- Design, implement, and maintain scalable ML pipelines for data ingestion, preprocessing, feature engineering, training, and serving.
- Build and optimize feature stores, ensuring efficient access to large-scale structured, unstructured, and multi-modal data.
- Deploy and monitor models in production, ensuring high reliability, low latency, and real-time serving capabilities.
- Develop tools for A/B testing, experiment tracking, and automated evaluation of recommendation models.
- Optimize ML workflows for cost-efficiency and scalability across distributed systems (e.g., Spark, Flink, TensorFlow Serving, TorchServe).
- Collaborate with product managers, engineers, and data scientists to translate research innovations into business-impacting production solutions.
- Ensure best practices for reproducibility, testing, and monitoring of ML models.
### *Requirements*
- Master’s degree (or Bachelor’s with strong experience) in Computer Science, Software Engineering, or a related field.
- 2+ years of experience in machine learning engineering, MLOps, or data engineering.
- Strong programming skills in Python and familiarity with Java is a plus.
- Experience with ML frameworks and libraries (PyTorch, TensorFlow, XGBoost, LightGBM, etc.).
- Solid knowledge of distributed data processing systems (Spark, Flink, Hadoop) and query languages (SQL/HiveQL).
- Hands-on experience with ML pipeline orchestration tools (e.g., Airflow, Kubeflow, MLflow).
- Experience with containerization and deployment platforms (Docker, Kubernetes, TensorFlow Serving, TorchServe).
- Familiarity with experiment design, A/B testing, and model monitoring in production.
- Good communication and collaboration skills to work in cross-functional teams.
### *Preferred Qualifications*
- Mandarin speaking is preferable.
- Prior experience in recommender systems, computational advertising, or large-scale ML platforms.
- Knowledge of real-time streaming data systems (Kafka, Pulsar).
- Understanding of model explainability, fairness, and monitoring in production ML systems.
- Contributions to open-source ML tools or large-scale ML projects.
ONLY CANDIDATES WHO MAY LIVE AND WORK IN IRELAND WITHOUT RESTRICTION CAN BE CONSIDERED FOR THIS POSITION.
DUE TO THE HIGH VOLUME OF REPLIES, ONLY CANDIDATES WHO ARE SHORTLISTED FOR INTERVIEWS WILL BE CONTACTED.
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