Senior Recommendation System Engineer

Amit érdemes tudni

• Remote · Ismeretlen
• Távmunka, de a magyarországi jogosultság nem egyértelmű.
• Szint: Senior
• Fizetés: nem közölt

Helyszín és jogosultság

Távmunka, de a magyarországi jogosultság nem egyértelmű.

Kuala Lumpur, Malaysia

Megbízhatóság: 48%. A végső döntés a munkáltatóé – mindig olvasd el az eredeti hirdetést.

Pozíció

About Us

Established in 2018, Bybit is one of the world’s leading cryptocurrency exchanges and digital financial platforms, serving over 80 million users across more than 200 countries and regions. Powered by world-class technology and a user-first mindset, Bybit delivers a seamless ecosystem across trading, payments, wealth management, custody, institutional services, and Web3 — connecting users to the future of digital finance.

Our core values define how we build. We listen, care and improve to create products and experiences that put users first. Backed by a global team of ambitious builders, problem-solvers, and innovators, we foster a high-performance and fast-moving environment where talent is empowered to drive real impact at the global scale. Supported by 24/7 multilingual customer service and a strong commitment to innovation, we are shaping the future of finance through technology, collaboration, and bold execution.

Today, Bybit is recognized as one of the most trusted and transparent platforms in the digital asset industry, continuing to expand its global presence while building the infrastructure for the next generation of financial services.

Key Responsibilities

1. Recommendation Engine & Full-Pipeline Development

Multi-stage Engine Development: Own the development and refactoring of high-concurrency, low-latency recommendation serving engines, powering the full pipeline of multi-channel recall (two-tower/collaborative filtering/ANN vector retrieval) → coarse ranking → fine ranking → re-ranking.

Compute Tiering & Strategy Engine: Implement dynamic compute trimming and degradation mechanisms for dynamic Ul personalization and global strategy dispatch, ensuring core engine stability under extreme traffic spikes.

2. Real-time Feature Engineering & Data Consistency Governance

Real-time Feature Pipeline: Build high-throughput, low-latency real-time feature streams on Kafka/Flink, enabling minute-level/second-level user pehavioral feature updates and dynamic sliding-window aggregations.

Feature Store: Contribute to the development of unified online/offline feature storage with stream-batch convergence architecture; deeply govern industrial-grade pain points such as "online/offline feature inconsistency" and "feature time-travel leakage," systematically improving online/offline feature consistency.

3. Large-scale Retrieval & Vector Infrastructure Optimization

Vector Retrieval System: Own the construction and optimization of large-scale ve

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