Senior Staff Algorithm Engineer, Recommendation

About the Role

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