Backend Engineer Graduate (Recommendation Architecture) - 2026 Start (PhD)

Employment Type: Regular

Job Code: A18700A

Responsibilities

1. Strategy Management and Optimization:
   Build an intelligent system to achieve standardized definition of recommendation strategies, long‑term and offline evaluation, automatic identification and retirement of ineffective strategies, and removal of related code configurations.
2. Adaptive Tuning and Fault Diagnosis:
   Leverage large model capabilities to optimize parameters and configurations of systems and underlying components for diverse business loads in recommendation systems. Explore adaptive fault diagnosis solutions to provide a global perspective for fault tracking, localization, and analysis.
3. Cost‑Efficiency Balance:
   Address the high costs of model training and operation when applying generative technologies to recommendation systems, balancing costs and efficiency to achieve effective recommendation within limited resources.
4. Cross‑Domain Data Processing:
   Handle massive heterogeneous data in horizontal cross‑domain scenarios (e.g., e‑commerce), improve and ensure data quality and accuracy, standardize data supply for cross‑domain recommendation models, and enable low‑cost cross‑terminal services. Ensure data privacy, security, and compliance.
5. Data Storage and Quality Enhancement:
   Develop low‑cost, high‑performance storage engines, design flexible schema evolution mechanisms, achieve high‑concurrency real‑time data writing and training‑inference consistency. Build data‑model correlation analysis tools and automated training data processing pipelines based on the DCAI concept.
6. Multimodal Data and Heterogeneous Computing:
   Construct a multimodal data heterogeneous computing framework for recommendation systems to solve challenges in data reading, framework integration, and high‑performance operator orchestration, improving data processing and model training efficiency. Establish a developer ecosystem centered on Python.
7. Large‑scale computing Model Efficiency Optimization for Recommendation:
   With continuous breakthroughs of large models in CV/NLP/multimodal fields and towards AGI, large computing‑driven recommendation scenarios enable models to more comprehensively and profoundly understand user preferences, thereby better interpreting user needs, excavating latent interests, and delivering superior user experiences. Balance computing overhead and effectiveness gains requires in‑depth Co‑Design by architecture and algorithm engineers.

Qualifications

Minimum Qualifications:

• Completed or completing a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• In‑depth research results and extensive practical experience in relevant fields such as natural language processing, computer vision, data modeling, or algorithm optimization.
• Excellent programming abilities with strong command of data structures and fundamental algorithms. Proficiency in C/C++ is required for traditional coding roles; proficiency in Python is required for intelligent coding roles.

Preferred Qualification:

• Effective communication and collaboration skills with team members, such as algorithm engineers, data analysts, and product managers, to explore new technologies and drive innovation in e‑commerce generative recommendation systems.

By submitting an application for this role, you accept and agree to our global applicant privacy policy.