Job Description

Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About the Role You will own the technical direction of OKX's next-generation social feed recommendation system — evolving it from a content feed into a unified recommendation engine that surfaces both content and platform features. Your decisions directly shape the experience of tens of millions of users and drive platform trading conversion. Responsibilities - Elevate the Ranking System — Drive continuous ranking model iteration with measurable impact on user retention and trading conversion - Unify User Understanding — Build a cross-domain intent framework spanning content consumption, feature usage, and search, shifting the system from "what users clicked" to "what users are trying to do" - Define the Technical Roadmap — Chart and execute a 12–24 month evolution from Transformer-based ranking toward generative recommendation (sequence generation + preference alignment) - Pioneer the Agent Paradigm — Integrate recommendation and search capabilities into an LLM Agent framework, enabling proactive intent fulfillment rather than passive content delivery Requirements - Background — Master's or above in CS / Math from a top university; 8+ years of experience with 5+ years in core recommendation / search roles; track record of owning end-to-end recommendation pipelines at 10M+ DAU scale - User Intent & Profiling (Core) — Experience designing unified intent representations across heterogeneous domains (content / feature / search); ability to fuse real-time behavioral signals with long-term stable preferences; hands-on experience with tiered user profile systems (cold-start → interest exploration → stable preference) - Transformer & Ranking (Core) — Deep understanding of Attention mechanisms in sequential behavior modeling and their limitations (DIN / SIM / HSTU evolution); ability to propose independent solutions under engineering constraints; proficiency in Listwise losses (ListMLE / Softmax Loss) and joint multi-candidate ranking - Multi-Task Training (Core) — Expert-level knowledge of MMoE / PLE / ESMM and gradient conflict identification and mitigation; ability to design composite loss function frameworks from scratch; proven methodology for bridging offline metrics (AUC / NDCG) and online business KPIs - Business Attribution (Core) — Hands-on Uplift Modeling experience; proficiency in Position / Selection Bias correction and prediction probability Calibration - Generative Recommendation (Strong Plus) — Understanding of Semantic Tokenization (FSQ / RQ-VAE) and conditional sequence generation; working-level knowledge of RLHF / DPO applied to recommendation systems - Recommendation & Search Agent (Strong Plus) — Engineering experience with LLM Agent frameworks (Tool Use / ReAct); ability to define the collaboration boundary between Agent-based and traditional recommendation; experience designing systems that translate natural language intent into structured retrieval requests - Engineering — Large-scale distributed training (10B+ parameter models); real-time feature engineering (Flink / Kafka); inference optimization under strict latency SLA Bonus First-author publication at RecSys / KDD / WWW ｜ Bandit / RL production deployment ｜ Background in fintech / crypto Perks & Benefits - Competitive total compensation package - L&D programs and education subsidy for employees' growth and development - Various team building programs and company events - Wellness and meal allowance - Comprehensive healthcare schemes for employees and dependants - More that we love to tell you along the process! Please note that Hong Kong is a group-level service hub, and OKX does not carry on a business of operating a virtual asset trading platform in Hong Kong. Notice: All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. Information collected and processed as part

Requirements

Background — Master's or above in CS / Math from a top university; 8+ years of experience with 5+ years in core recommendation / search roles; track record of owning end-to-end recommendation pipelines at 10M+ DAU scale

User Intent & Profiling (Core) — Experience designing unified intent representations across heterogeneous domains (content / feature / search); ability to fuse real-time behavioral signals with long-term stable preferences; hands-on experience with tiered user profile systems (cold-start → interest exploration → stable preference)

Transformer & Ranking (Core) — Deep understanding of Attention mechanisms in sequential behavior modeling and their limitations (DIN / SIM / HSTU evolution); ability to propose independent solutions under engineering constraints; proficiency in Listwise losses (ListMLE / Softmax Loss) and joint multi-candidate ranking

Multi-Task Training (Core) — Expert-level knowledge of MMoE / PLE / ESMM and gradient conflict identification and mitigation; ability to design composite loss function frameworks from scratch; proven methodology for bridging offline metrics (AUC / NDCG) and online business KPIs

Business Attribution (Core) — Hands-on Uplift Modeling experience; proficiency in Position / Selection Bias correction and prediction probability Calibration

Generative Recommendation (Strong Plus) — Understanding of Semantic Tokenization (FSQ / RQ-VAE) and conditional sequence generation; working-level knowledge of RLHF / DPO applied to recommendation systems

Recommendation & Search Agent (Strong Plus) — Engineering experience with LLM Agent frameworks (Tool Use / ReAct); ability to define the collaboration boundary between Agent-based and traditional recommendation; experience designing systems that translate natural language intent into structured retrieval requests

Engineering — Large-scale distributed training (10B+ parameter models); real-time feature engineering (Flink / Kafka); inference optimization under strict latency SLA

Responsibilities

- Elevate the Ranking System — Drive continuous ranking model iteration with measurable impact on user retention and trading conversion

- Unify User Understanding — Build a cross-domain intent framework spanning content consumption, feature usage, and search, shifting the system from "what users clicked" to "what users are trying to do"

- Define the Technical Roadmap — Chart and execute a 12–24 month evolution from Transformer-based ranking toward generative recommendation (sequence generation + preference alignment)

- Pioneer the Agent Paradigm — Integrate recommendation and search capabilities into an LLM Agent framework, enabling proactive intent fulfillment rather than passive content delivery

Senior Staff Algorithm Engineer, Recommendation

Job Description

Requirements

Responsibilities

Skills Required

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