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

ByteDance; Singapore; Vor Ort; SGD 90.000 - 130.000; Vollzeit

Zusammenfassung

ByteDance is seeking a PhD-level researcher to join our Recommendation Architecture Team in Singapore. You will build and optimize the system architecture to deliver stable, scalable, high-availability experiences for users, collaborating with algorithm teams to boost effectiveness while reducing costs.

The role emphasizes exploring data-centric AI, multimodal data, and large-model optimization across online/offline data flows, with a focus on practical, scalable solutions for e-commerce and

Qualifikationen

• PhD in Software Development/CS/Engineering completed or near completion.
• Strong research and practical experience in NLP/vision/data modeling or algorithm optimization.
• Proficient in data structures and fundamental algorithms; C/C++ for traditional coding roles and Python for AI-focused roles.

Aufgaben

• Build an intelligent system to standardize and evaluate recommendation strategies; manage long-term offline evaluation.
• Leverage large model capabilities to optimize system parameters for diverse workloads and diagnose faults.
• Balance costs of model training/operation with efficiency for high-quality recommendations.
• Handle cross-domain data processing to ensure data quality, privacy, and compliance across domains like e-commerce.
• Develop high-performance storage and data pipelines; explore data-model correlations and automated training data workflows.
• Construct a multimodal data framework and a Python-centric developer ecosystem to improve processing and model training efficiency.
• Explore large-scale computing model efficiency through architecture-algorithm co-design with engineers.

Kenntnisse

Python

C/C++

Algorithms

Data Structures

Machine Learning

Ausbildung

PhD in Computer Science

Jobbeschreibung

Responsibilities

Team Introduction

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.

We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.

Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume.

Responsibilities

• 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.

• 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 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 (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. Meanwhile, 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. Deeply explore the quantitative relationship between data quality and model prediction performance, and build data-model correlation analysis tools and automated training data processing pipelines based on the DCAI (Data-Centric AI) 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, improving data processing and model training efficiency. Establish a developer ecosystem centered on Python.

• Large-scale computing Model Efficiency Optimization for Recommendation:

With continuous breakthroughs of large models in CV/NLP/multimodal fields and even 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. Larger-scale recommendation models demand greater computing. To balance computing overhead and effectiveness gains requires in-depth Co-Design by architecture and algorithm engineers.

Qualifications

Minimum Qualifications:

• Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.


• Priority will be given to candidates with in-depth research results and extensive practical experience in relevant fields, such as outstanding performance in natural language processing, computer vision, data modeling, or algorithm optimization, etc.


• Excellent programming abilities with a strong command of data structures and fundamental algorithms. For traditional coding roles, proficiency in C/C++ is required; for intelligent coding roles, proficiency in Python is required.

Preferred Qualification:

• Ability to effectively communicate and collaborate 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.

About Us

Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Lemon8, CapCut and Pico as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content.

Why Join ByteDance

Inspiring creativity is at the core of ByteDance's mission. Our innovative products are built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and enrich life – a mission we work towards every day.

As ByteDancers, we strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. By constantly iterating and fostering an \"Always Day 1\" mindset, we achieve meaningful breakthroughs for ourselves, our Company, and our users. When we create and grow together, the possibilities are limitless. Join us.

Diversity & Inclusion

ByteDance is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At ByteDance, our mission is to inspire creativity and enrich life. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.