Recommendation System Architecture Engineer- Soaring Star Talent Program

undefined ago • All levels • System Design

Job Summary

Job Description

The ByteDance Recommendation Architecture Team is seeking an engineer for its Soaring Star Talent Program. This role involves designing and developing recommendation system architecture, ensuring stability, optimizing performance, and resolving bottlenecks. Key challenges include strategy management, adaptive tuning, cost-efficiency balance, cross-domain data processing, data storage, multimodal data handling, and large-scale model efficiency optimization. The ideal candidate will have a doctor's degree in AI, Computer Science, or Mathematics, excellent programming skills in C/C++ or Python, a strong foundation in machine learning, and familiarity with AGI technologies.
Must have:
  • Design and develop recommendation system architecture
  • Ensure system stability and high availability
  • Optimize online services and offline data streams
  • Resolve system bottlenecks and reduce cost overheads
  • Build recommendation and data middle-office
  • Build intelligent system for recommendation strategies
  • Leverage large models for system optimization and fault diagnosis
  • Balance costs and efficiency in generative recommendation systems
  • Handle massive heterogeneous cross-domain data
  • Develop low-cost, high-performance storage engines
  • Construct multimodal data heterogeneous computing framework
  • Co-design with algorithm engineers for large-scale models
  • Hold a doctor degree
  • Demonstrate excellent programming abilities
  • Possess strong command of data structures and algorithms
  • Proficiency in C/C++ or Python
  • Have a strong foundation in machine learning
  • Familiarity with latest AGI technologies
Good to have:
  • In-depth research results in AI, Computer Science, or Mathematics
  • Extensive practical experience in relevant fields
  • Outstanding performance in natural language processing
  • Outstanding performance in computer vision
  • Outstanding performance in data modeling
  • Outstanding performance in algorithm optimization
  • Strong engineering mindset to balance performance and cost
  • Ability to implement complex algorithms and build iterative models
  • Ability to build, train, and optimize machine learning models
  • Experience quickly validating and exploring AGI applications in e-commerce generative recommendation

Job Details

Responsibilities

Team Introduction: The ByteDance Recommendation Architecture Team is responsible for the design and development of the recommendation system architecture for ByteDance's related products. It ensures the stability and high availability of the system, optimizes the performance of online services and offline data streams, resolves system bottlenecks, and reduces cost overheads. The team also abstracts the common components and services of the system, builds the recommendation middle - office and data middle - office to support the rapid incubation of new products and enable ToB services.

Project Challenges:

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

1. Got a doctor degree;

2. Preferred fields: Artificial Intelligence, Computer Science, Mathematics, and related interdisciplinary majors;

3. Academic achievements: 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.;

4. Coding skills: 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. Candidates are required to use these languages to implement complex algorithms and build iterative models. Candidates should also have a strong engineering mindset with the ability to balance performance and cost;

5. Machine learning skills: Strong foundation in machine learning, familiarity with commonly used models (e.g., decision trees, support vector machines), and the ability to build, train, and optimize machine learning models. Candidates should also be familiar with the latest AGI technologies, with the ability to quickly validate and explore their applications in e-commerce generative recommendation;

6. Communication and collaboration: 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.

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About The Company

Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok 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.
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