Backend Engineer - Search, Ads & Recommendation Mandarin Required

Fuku•New York, NY

2d•Onsite

About The Position

Our client is a rapidly scaling global consumer internet platform with hundreds of millions of users across content, community, e-commerce, and advertising ecosystems. As the company expands internationally, it is building out its engineering capabilities in New York to support its growing global user base. This role is part of the core engineering organization responsible for the systems powering Search, Advertising, and Recommendation across the company's main platform and international product. You will work on large-scale online systems operating at tens-of-millions-of-users scale, tackling challenges in distributed systems, high concurrency, system architecture, latency, and performance optimization. The team is also integrating LLMs and multimodal AI capabilities into production, offering a unique opportunity to work at the intersection of large-scale backend engineering, Search/Recommendation/Ads, and AI-native applications.

Requirements

Bachelor’s degree or above in Computer Science or a related field, or equivalent practical experience.
Minimum 3+ years of experience building large-scale backend systems and distributed systems.
Proficient in at least one of: Java, C, or C++.
Strong software engineering fundamentals.
High standards for code quality, system design, and engineering discipline.
Deep understanding of high-concurrency distributed systems, high-availability architecture, and large-scale system design.
Strong practical experience in system scalability, performance optimization, and latency optimization.
Mature methodology for identifying and resolving scalability and performance bottlenecks.
Hands-on experience putting LLMs into production.
Practical experience with one or more of: RAG, agents/agent workflows, prompt engineering, systematic LLM pipeline development.
Ability to combine AI capabilities with Search, Advertising, and Recommendation products.
Experience designing reliable production systems around LLM or multimodal capabilities.
Experience developing products or systems serving tens of millions of users.
Ability to independently own architecture design, key domain modeling, and critical backend systems.
Strong system abstraction and architecture design capabilities.
Ability to identify architectural risks early and proactively drive them to resolution.
Strong business awareness and technical judgment.
Strong sense of ownership and ability to independently drive complex technical projects.
Effective cross-team collaborator.
Comfortable working closely with algorithm engineers, machine learning teams, product teams, and backend engineering teams.
Proficiency in both Mandarin Chinese and English is mandatory
Comfortable collaborating across globally distributed engineering teams.

Nice To Haves

Experience building or operating large-scale Search, Advertising, or Recommendation systems.
Experience supporting international products or overseas engineering projects.

Responsibilities

Design, build, and continuously optimize the core engine and business architecture powering Search, Advertising, and Recommendation.
Build systems supporting both the company's main platform and international product.
Develop highly scalable online services capable of supporting tens to hundreds of millions of users.
Lead the productionization of LLM and multimodal capabilities across Search, Recommendation, and Advertising.
Own service design, backend development, end-to-end latency optimization, and system performance optimization.
Build production-grade AI applications and infrastructure involving LLM-powered features, RAG pipelines, agent workflows, prompt engineering, and systematic LLM pipelines.
Ensure AI-powered systems operate reliably and efficiently at scale.
Build the engineering bridge between model capabilities and real-world product experiences.
Work closely with algorithm and machine learning teams to bring model capabilities into production.
Design backend systems capable of supporting large-scale online Search, Ads, and Recommendation workloads.
Optimize end-to-end architecture for high concurrency, high availability, low latency, scalability, and production reliability.
Design and abstract shared engineering frameworks and platform components for reuse across multiple business lines.
Reduce duplicated engineering work across Search, Recommendation, and Ads.
Improve overall engineering efficiency, system quality, and maintainability.
Identify architectural risks early and proactively drive technical solutions through to production.