Senior Recommendation System Engineer

<div class="content-intro"><p><strong>About Us</strong></p> <div data-page-id="HladdPLXIoNz4nxkqkCuk4rAs8v" data-lark-html-role="root" data-docx-has-block-data="false"> <div class="ace-line ace-line old-record-id-XCikdRR2xoZIjdxtCKcuOflrs8d">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.</div> <div class="ace-line ace-line old-record-id-IF06dI0MPoBwYfxbZ4zui13HsoC">&nbsp;</div> <div class="ace-line ace-line old-record-id-X5EbdOwvMoYuOmxQBbuueUEDsDd">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.</div> <div class="ace-line ace-line old-record-id-LtVud2dO7oglqJxxZoOucqjJsPc">&nbsp;</div> <div class="ace-line ace-line old-record-id-RMHkdm4bPo66l3xMVO8ui1o8sle">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.</div> </div></div><div class="richTextContainer text-align-left" data-partial-quote-enabled="true" data-lark-html-role="root"> <div class="richTextDocs" data-eleid="1"> <div class="rich-text-paragraph" data-eleid="2"><strong><span class="text-only" data-eleid="3"><span class="text-only">Key Responsibilities</span></span></strong></div> <div class="rich-text-paragraph" data-eleid="4"><span class="text-only" data-eleid="5"><span class="text-only">1. <strong>Recommendation Engine &amp; Full-Pipeline Development</strong></span></span></div> <ul class="richTextDocs-unOrderList richTextDocs-unOrderList-disc" data-eleid="6"> <li class="richTextDocs-listItem" data-eleid="7"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="8"><span class="text-only">Multi-stage Engine Development:</span></span></strong><span class="text-only" data-eleid="9"><span class="text-only"> 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.</span></span></div> </li> <li class="richTextDocs-listItem" data-eleid="10"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="12"><span class="text-only">Compute Tiering &amp; Strategy Engine: </span></span></strong><span class="text-only" data-eleid="13"><span class="text-only">Implement dynamic compute trimming and degradation mechanisms for dynamic Ul personalization and global strategy dispatch, ensuring core engine stability under extreme traffic spikes.</span></span></div> </li> </ul> <div class="rich-text-paragraph" data-eleid="14"><strong><span class="text-only" data-eleid="15"><span class="text-only">2. Real-time Feature Engineering &amp; Data Consistency Governance</span></span></strong></div> <ul class="richTextDocs-unOrderList richTextDocs-unOrderList-disc" data-eleid="16"> <li class="richTextDocs-listItem" data-eleid="17"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="18"><span class="text-only">Real-time Feature Pipeline:</span></span></strong><span class="text-only" data-eleid="19"><span class="text-only"> 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.</span></span></div> </li> <li class="richTextDocs-listItem" data-eleid="20"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="21"><span class="text-only">Feature Store: </span></span></strong><span class="text-only" data-eleid="22"><span class="text-only">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.</span></span></div> </li> </ul> <div class="rich-text-paragraph" data-eleid="23"><strong><span class="text-only" data-eleid="24"><span class="text-only">3. Large-scale Retrieval &amp; Vector </span><span class="text-only text-with-abbreviation text-with-abbreviation-bottomline">Infrastructure</span><span class="text-only"> Optimization</span></span></strong></div> <ul class="richTextDocs-unOrderList richTextDocs-unOrderList-disc" data-eleid="25"> <li class="richTextDocs-listItem" data-eleid="26"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="27"><span class="text-only">Vector Retrieval System: </span></span></strong><span class="text-only" data-eleid="28"><span class="text-only">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.</span></span></div> </li> <li class="richTextDocs-listItem" data-eleid="29"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="30"><span class="text-only">Multi-source Heterogeneous Indexing: </span></span></strong><span class="text-only" data-eleid="31"><span class="text-only">Build unified Embedding Pipelines and high-performance inverted index services covering trading products, news, KOL content, </span><span class="text-only text-with-abbreviation text-with-abbreviation-bottomline">on-chain</span><span class="text-only"> signals, and other heterogeneous data sources.</span></span></div> </li> </ul> <div class="rich-text-paragraph" data-eleid="32"><strong><span class="text-only" data-eleid="33"><span class="text-only">4. High-performance Model Inference &amp; Compute Optimization</span></span></strong></div> <ul class="richTextDocs-unOrderList richTextDocs-unOrderList-disc" data-eleid="34"> <li class="richTextDocs-listItem" data-eleid="35"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="36"><span class="text-only">Deep Model Engineering: </span></span></strong><span class="text-only" data-eleid="37"><span class="text-only">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).</span></span></div> </li> <li class="richTextDocs-listItem" data-eleid="38"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="39"><span class="text-only">Inference Graph &amp; Compute Governance: </span></span></strong><span class="text-only" data-eleid="40"><span class="text-only">Solve compute explosion in multi-task/multi-objective online inference through inference optimization (quantization, graph optimization, batching strategies) - targeting fine-ranking P99 latency < 200ms.</span></span></div> </li> </ul> <div class="rich-text-paragraph" data-eleid="41"><strong><span class="text-only" data-eleid="42"><span class="text-only">5. System Performance, Global Observability &amp; Experimentation Infrastructure</span></span></strong></div> <ul class="richTextDocs-unOrderList richTextDocs-unOrderList-disc" data-eleid="43"> <li class="richTextDocs-listItem" data-eleid="44"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="45"><span class="text-only">High Availability &amp; SLO: </span></span></strong><span class="text-only" data-eleid="46"><span class="text-only">Ensure global recommendation service P99 latency < 200ms and system availability > 99.9%; write comprehensive overload protection, thread isolation, and disaster recovery degradation code.</span></span></div> </li> <li class="richTextDocs-listItem" data-eleid="47"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="48"><span class="text-only">Global Distributed Tracing: </span></span></strong><span class="text-only" data-eleid="49"><span class="text-only">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.</span></span></div> </li> <li class="richTextDocs-listItem" data-eleid="50"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="51"><span class="text-only">Experimentation Platform Engineering: </span></span></strong><span class="text-only" data-eleid="52"><span class="text-only">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.</span></span></div> </li> </ul> <div class="rich-text-paragraph" data-eleid="53">&nbsp;</div> <div class="rich-text-paragraph" data-eleid="54"><strong><span class="text-only" data-eleid="55"><span class="text-only">Requirements</span></span></strong></div> <ul class="richTextDocs-unOrderList richTextDocs-unOrderList-disc" data-eleid="56"> <li class="richTextDocs-listItem" data-eleid="57"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="58"><span class="text-only">Industrial-grade Hands-on Experience: </span></span></strong><span class="text-only" data-eleid="59"><span class="text-only">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.</span></span></div> </li> <li class="richTextDocs-listItem" data-eleid="60"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="61"><span class="text-only">Mastery of Engineering Foundations: </span></span></strong><span class="text-only" data-eleid="62"><span class="text-only">Exceptionally solid low-level </span><span class="text-only text-with-abbreviation text-with-abbreviation-bottomline">CS</span><span class="text-only"> 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.</span></span></div> </li> <li class="richTextDocs-listItem" data-eleid="63"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="64"><span class="text-only">Deep Expertise in Big Data &amp; Retrieval: </span></span></strong><span class="text-only" data-eleid="65"><span class="text-only">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.</span></span></div> </li> <li class="richTextDocs-listItem" data-eleid="66"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="67"><span class="text-only">Engineering Perspective on Algorithms: </span></span></strong><span class="text-only" data-eleid="68"><span class="text-only">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.</span></span></div> </li> <li class="richTextDocs-listItem" data-eleid="69"> <div class="richTextDocs-listItem__text"><strong><span class="text-only" data-eleid="70"><span class="text-only">Excellent System Design Capability: </span></span></strong><span class="text-only" data-eleid="71"><span class="text-only">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.</span></span></div> </li> </ul> </div> </div><div class="content-conclusion"><p><strong>Why Join Us</strong><br>At Bybit, we are committed to fostering a supportive and enriching work environment.&nbsp;<br>Our benefits include:<br>- Study Growth Fund: We support your professional development and continuous learning.<br>- Internal Events: Participate in regular team-building activities, workshops, and events designed to promote collaboration and innovation.<br>- Global Collaboration: Be part of a diverse, international team, working alongside colleagues from around the world.<br>- Career Advancement: Access opportunities for growth and advancement within a rapidly expanding global company.<br>- Internal Mobility: Grow with us- Your long-term development is important to us. We offer internal job opportunities to help build your career path.</p></div>