Engineering Lead - Algorithm Data Engineering
Posted on March 23, 2026 by Shopee
- Singapore, Singapore
- N/A
- nan
Department Engineering and Technology
LevelExperienced (Team Lead)
LocationSingapore
The Engineering and Technology team is at the core of the Shopee platform development. The team is made up of a group of passionate engineers from all over the world, striving to build the best systems with the most suitable technologies. Our engineers do not merely solve problems at hand; We build foundations for a long-lasting future. We don't limit ourselves on what we can or can't do; we take matters into our own hands even if it means drilling down to the bottom layer of the computing platform. Shopee's hyper-growing business scale has transformed most "innocent" problems into huge technical challenges, and there is no better place to experience it first-hand if you love technologies as much as we do.
About the Team:
The mission of the Marketplace Intelligence and Data team is to build sustainable, efficient data and intelligence products that power Shopee’s business growth. The team is responsible for Shopee’s e-commerce data warehouse, merchant and operations data products, end-to-end traffic data, product algorithms (including product listing, governance, content optimization, SPU cataloging and price comparison), marketing algorithms (including merchant onboarding, assortment, and recommendations), review algorithms, user profiling, as well as foundational AI capabilities such as machine translation, speech processing, computer vision, and identity verification.
As the core horizontal data team supporting all algorithm teams within Marketplace Intelligence and Data, the Algo Data Team aims to be the most reliable strategic data partner for algorithm development. We are committed to delivering efficient, stable, and high-quality data services that accelerate algorithm iteration and transform data into strong commercial value for Shopee.
Job Description:
- Collaborate closely with the MPID algorithm team, fully participating in model training, deployment, and iteration. Drive high-quality feature engineering and efficient data pipeline development.
- Design, deploy, and integrate online data pipeline systems. Establish feedback loops for performance evaluation and data return flows, enabling continuous optimization and performance improvement of models in real-world scenarios.
- Own the end-to-end data engineering lifecycle for large model training, including multi-source data ingestion, cleaning, labeling, automated data production, and distributed pipeline design and optimization. Support different stages such as pretraining, supervised fine-tuning (SFT), and parameter-efficient fine-tuning (e.g., LoRA).
- Build and maintain the data foundation for AI systems. Manage the full data lifecycle, including data taxonomy design, schema standardization, and improving data quality, diversity, and usability. Develop comprehensive data quality evaluation and monitoring systems to enhance model performance across multilingual and multi-domain applications.
- Lead the data engineering team and drive cross-functional collaboration with algorithm teams. Ensure high-quality project delivery and foster team growth and professional development.
Requirements:
- Bachelor‘s degree or above in Computer Science, Artificial Intelligence, Data Engineering, or a related field, or equivalent practical experience.
- Strong expertise in data engineering and distributed systems. Proficient in tools and frameworks such as Spark, Flink, Ray, Python, Java, and SQL.
- At least 5 years of hands-on experience building and operating large-scale data pipelines, with a proven track record of handling PB-scale data processing, optimization, and automation.
- At least 3 years of experience in team management, with strong leadership, communication, and project execution skills.
- Solid understanding of machine learning workflows, with the ability to collaborate effectively with algorithm teams and contribute to feature engineering and data sampling strategies.
- Experience in building end-to-end LLM data platforms, such as Data Lake or Data Flywheel systems or experience or research background in data selection, synthetic data generation, LLM-based evaluation (LLM-as-a-Judge), or agent evaluation frameworks.
Advertised until:
April 22, 2026
Are you Qualified for this Role?
Click Here to Tailor Your Resume to Match this Job
Share with Friends!
Similar Internships
No similar Intern Jobs at the Moment!
Recent Internships
Get Email Updates!
