Senior Staff Algorithm Engineer, Recommendation
External
Okx · Asia PacificPrepare for this interview
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About the role
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. 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
- 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
- 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
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