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

ByteDance; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

ByteDance is looking for a dedicated PhD graduate to join their Recommendation Architecture Team in Singapore. This role focuses on strategy management, adaptive tuning, cross-domain data processing, and optimizing costs within the recommendation system. Ideal candidates must have robust programming skills, especially in Python and C/C++, as well as the ability to work collaboratively with various team members to drive technological innovation in e-commerce generative systems.

The position requires commitment to an onboarding date by the end of 2026 and prioritizes applicants with extensive research experience in related fields such as data modeling and algorithm optimization.

Qualifications

• Commit to onboarding date by end of 2026.
• PhD in Software Development or related field necessary.
• Research experience in NLP, CV, or algorithm optimization preferred.

Responsibilities

• Build intelligent systems for recommendation strategy management.
• Optimize parameters for diverse business loads.
• Enhance data quality and ensure compliance.
• Develop frameworks for multimodal data processing.
• Collaborate with engineers for model efficiency optimization.

Skills

Programming abilities

Data structures and algorithms

C/C++ proficiency

Python proficiency

Communication and collaboration

Education

PhD in Software Development, Computer Science, or related field

Tools

Python

C/C++

Job description

Our Recommendation Architecture Team is responsible for building and optimizing the architecture of the recommendation system to provide the most stable and best experience for users. The team focuses on optimizing the recommendation system architecture, ensuring stability and high availability, and improving the performance of both online services and offline data flows. Collaborating with the algorithm team, we work to enhance recommendation effectiveness and user experience, boost system performance while reducing costs, build data and service mid‑platforms, and realize flexible and scalable high‑performance storage and computing systems.

Responsibilities

• Strategy Management and Optimization: Build an intelligent system to standardize recommendation strategies, perform long‑term and offline evaluation, automatically identify and retire ineffective strategies, and remove related code configurations.
• Adaptive Tuning and Fault Diagnosis: Leverage large model capabilities to optimize parameters and configurations of systems and underlying components for diverse business loads, and explore adaptive fault diagnosis solutions to provide global perspective for fault tracking, localization, and analysis.
• 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.
• Cross‑Domain Data Processing: Handle massive heterogeneous data in horizontal cross‑domain scenarios, improve and ensure data quality and accuracy, standardize data supply for cross‑domain recommendation models, enable low‑cost cross‑terminal services, and ensure data privacy, security, and compliance.
• 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, and build data‑model correlation analysis tools and automated training‑data processing pipelines based on the DCAI concept.
• 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, improve data processing and model training efficiency, and establish a developer ecosystem centered on Python.
• Large‑scale Computing Model Efficiency Optimization for Recommendation: Co‑design with architecture and algorithm engineers to balance computing overhead and effectiveness gains for large‑scale recommendation models powered by large models in CV, NLP, and multimodal fields.

Qualifications

• Must be able to commit to an onboarding date by the end of 2026 and provide graduation date.
• Individuals completing or who have recently completed a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Priority to candidates with 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 a strong command of data structures and fundamental algorithms; proficiency in C/C++ is required for traditional coding roles, while proficiency in Python is required for intelligent coding roles.
• Ability to effectively communicate and collaborate with team members—including algorithm engineers, data analysts, and product managers—to explore new technologies and drive innovation in e‑commerce generative recommendation systems.

This position is part of the Recommendation Architecture Team at ByteDance.