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

TIKTOK PTE. LTD.; Singapore; On-site; SGD 70,000 - 90,000; Full time

Job summary

A leading technology company in Singapore seeks final year PhD graduates to join the Recommendation Architecture Team. The role involves optimizing the recommendation system architecture for better user experience and data processing efficiency. Successful candidates will tackle complex challenges and collaborate with diverse teams, offering a flexible working environment and competitive compensation.

Benefits

Yoga and fitness

Stock options

Career growth opportunity

Competitive compensation

Flexible hours

Qualifications

• Final year PhD graduates required, prior research and practical experience preferred.
• Proficient in C/C++ for traditional roles, Python for intelligent roles.
• Excellent communication and collaboration skills.

Responsibilities

• Build intelligent recommendation strategies and optimize them.
• Conduct adaptive tuning and fault diagnosis for systems.
• Prepare cross-domain data processing and ensure quality.

Skills

Programming in Python

Data structures

Algorithm optimization

Natural language processing

Computer vision

Education

PhD in Computer Science or related field

Tools

Python

C/C++

Job description

About TikTok

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us

Inspiring creativity is at the core of TikTok's mission. Our innovative product is 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 bring joy - a mission we work towards every day.

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. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. 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

TikTok 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 TikTok, our mission is to inspire creativity and bring joy. 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.

Job highlights

Yoga and fitness, Stock options, Positive team atmosphere, Career growth opportunity, Paid leave, Flat organization, 100+ mil users, Industry experts, Competitive compensation, Flexible hours

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 TikTok 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 TikTok.

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.

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to TikTok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

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 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. Meanwhile, 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. 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.

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 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:

- Final year Phd graduates in Computer Science, engineering or quantitative field.

- 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.

By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy

If you have any questions, please reach out to us at [e-mail]