Principal AIgorithm Engineer, Search and Recommendation

Who We Are

About the Opportunity

What You’ll Be Doing 

• Drive continuous ranking model iteration with measurable, attributable impact on user retention and trading conversion
• Build a cross-domain intent framework spanning content consumption, feature usage, and search — shifting the system from tracking what users clicked to understanding what users are trying to do
• Chart and execute the technical evolution from Transformer-based sequential ranking toward generative recommendation, including sequence generation and preference alignment
• Integrate recommendation and search capabilities into an LLM Agent framework, moving from passive content delivery to proactive intent fulfillment
• Mentor senior engineers and help define the broader recommendation research and engineering strategy

What We Look For In You 

• Master's or above in Computer Science, Mathematics, or a related field from a top university; 8+ years of industry experience with 5+ years in core recommendation or search roles
• Proven end-to-end ownership of production recommendation pipelines at 10M+ DAU scale
• User Intent & Profiling (Core) — Hands-on experience designing unified intent representations across heterogeneous domains (content / feature / search); demonstrated ability to fuse real-time behavioral signals with long-term stable preferences; experience building tiered user profile systems across the full cold-start → interest exploration → stable preference lifecycle
• Transformer & Sequential Ranking (Core) — Deep, practitioner-level command of Attention mechanisms in sequential behavior modeling and their production limitations (DIN / SIM / HSTU evolution); ability to propose independent architectural solutions under real engineering constraints; proficiency in Listwise loss functions (ListMLE / Softmax Loss) and joint multi-candidate ranking
• Multi-Task Training (Core) — Expert-level knowledge of MMoE / PLE / ESMM architectures; hands-on experience identifying and resolving gradient conflicts; ability to design composite loss function frameworks from scratch; proven methodology for closing the gap between offline metrics (AUC / NDCG) and live business KPIs
• Business Attribution (Core) — Hands-on Uplift Modeling experience; proficiency in Position / Selection Bias correction and prediction probability calibration

Nice to Haves

•  Familiarity with Semantic Tokenization (FSQ / RQ-VAE), conditional sequence generation, or RLHF / DPO applied to recommendation systems
• Engineering experience with LLM Agent frameworks (Tool Use / ReAct) and designing the collaboration boundary between agent-based and traditional recommendation
• Hands-on experience with large-scale distributed training (10B+ parameter models), real-time feature pipelines (Flink / Kafka), and inference optimization under strict latency SLAs
• Experience in fintech, Web3, or crypto platforms

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 allowances
• Comprehensive healthcare schemes for employees and dependants
• More that we love to tell you along the process!