Senior Recommendation System Engineer at Bybit – Kuala Lumpur | Jobyssey

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 vector retrieval systems (Faiss/Milvus/NSW) supporting candidate pools from tens of thousands to milions, lead index structure parameter tuning, achieving P99 retrieval latency < 100ms.
Multi-source Heterogeneous Indexing: Build unified Embedding Pipelines and high-performance inverted index services covering trading products, news, KOL content, on-chain signals, and other heterogeneous data sources.

4. High-performance Model Inference & Compute Optimization

Deep Model Engineering: Own high-performance online deployment and operator optimization of complex deep ranking models (e.g., DIN/SIM sequential models, MMoE/PLE multi-objective deep models).
Inference Graph & Compute Governance: Solve compute explosion in multi-task/multi-objective online inference through inference optimization (quantization, graph optimization, batching strategies) - targeting fine-ranking P99 latency < 200ms.

5. System Performance, Global Observability & Experimentation Infrastructure

High Availability & SLO: Ensure global recommendation service P99 latency < 200ms and system availability > 99.9%; write comprehensive overload protection, thread isolation, and disaster recovery degradation code.
Global Distributed Tracing: In complex multi-region/multi-site/multi-language cross-border IDC environments, build and maintain end-to-end distributed tracing and monitoring systems (e.g.,Jaeger/Prometheus), establishing minute-level root cause attribution and fault localization mechanisms.
Experimentation Platform Engineering: Contribute to A/B experiment traffic-splitting platform upgrades, engineering CUPED variance reduction and sequential testing mechanisms to reduce sample size requirements and accelerate algorithm iteration pipelines.

Requirements

Industrial-grade Hands-on Experience: 5+ years of recommendation system engineering at consumer-scale internet companies; must have deeply participated in or led architecture refactoring or launches of realtime recommendation systems serving tens of millions of users at scale. Proven experience building recommendation systems from 0→1 (not just maintaining mature systems) is strongly preferred.
Mastery of Engineering Foundations: Exceptionally solid low-level CS fundamentals; proficient in at least one of Go/Java/C++ (Go preferred given current stack; C++ experience a plus for future engine optimization); familiar with PyTorch/TensorFlow model online inference and deployment optimization.
Deep Expertise in Big Data & Retrieval: Hands-on with Spark/Flink/Kafka stack, with real experience solving stream computing latency and data backlog issues; proficient in Milvus/Faiss cluster deployment and tuning.
Engineering Perspective on Algorithms: Deep understanding of computational complexity and online bottlenecks of core algorithms (collaborative filtering, two-tower recall, multi-objective optimization MMoE/PLE); ability to interface smoothly with algorithm teams for high-quality, efficient engineering translation.
Excellent System Design Capability: Proven experience in actual development and core module design of recommendation platforms, feature platforms, experimentation platforms, or high-performance RPC frameworks at top-tier companies.

Why Join Us
At Bybit, we are committed to fostering a supportive and enriching work environment.
Our benefits include:
- Study Growth Fund: We support your professional development and continuous learning.
- Internal Events: Participate in regular team-building activities, workshops, and events designed to promote collaboration and innovation.
- Global Collaboration: Be part of a diverse, international team, working alongside colleagues from around the world.
- Career Advancement: Access opportunities for growth and advancement within a rapidly expanding global company.
- Internal Mobility: Grow with us- Your long-term development is important to us. We offer internal job opportunities to help build your career path.

Senior Recommendation System Engineer

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