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

ByteDance; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

ByteDance is seeking a high-caliber individual to work on advanced recommendation systems in Singapore. This role focuses on building intelligent systems, optimizing parameters, and enhancing data processing efficiency to deliver superior user experiences.

The ideal candidate holds a PhD in a related field and possesses deep expertise in programming, particularly in C/C++ and Python. You will collaborate with algorithm engineers and product managers to drive innovation in generative recommendation systems.

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.

Responsibilities

• Build an intelligent system for standardized recommendation strategies.
• Optimize parameters for diverse business loads in recommendation systems.
• Address high costs of model training in generative recommendation systems.
• Handle massive heterogeneous data for cross-domain recommendation models.
• Develop low-cost storage engines and ensure data quality.
• Construct multimodal data heterogeneous computing frameworks.
• Optimize model efficiency to better understand user preferences.

Skills

Natural language processing

Computer vision

Algorithm optimization

C/C++ programming

Python programming

Education

PhD in Software Development, Computer Science, or related field

Job description

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.

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