Tech Lead / Principal Engineer - Creator Agent Algorithm Infrastructure

The Arch-Global E-Commerce team develops large-scale recommendation, matching, and ranking algorithms for creator-commerce platforms, supports multiple teams such as Creator Marketplace, Creator Shop, and TikTok consumer experiences. We leverage machine learning and data-driven optimization to connect sellers, creators, and products more effectively, increase content-commerce supply, and accelerate e-commerce GMV growth. You will own the overall algorithm infrastructure roadmap for Creator Agent. Work in close partnership with the algorithm team to ensure cutting-edge agent capabilities can be delivered to the creator business. Responsibilities: 1. Lead the architectural development of core Agent algorithm capabilities, including but not limited to: - Agent orchestration framework: Build agent orchestration capabilities supporting complex business logic, based on LangGraph or in-house frameworks. - Agentic Search: Build intelligent retrieval architecture tailored to creator scenarios, enabling the Agent to proactively and iteratively gather information from product, creator, and content corpora. - Hierarchical memory systems: Design short-term, long-term, and episodic memory mechanisms, providing the algorithm team with foundational capabilities for personalized creator understanding. - Algorithm tuning infrastructure: Provide efficient training, evaluation, and iteration infrastructure for Agent RL, Memory RL, SFT, and additional frontier optimization directions (see below). 2. Continuously track and bring frontier Agent optimization directions into the team, including but not limited to: - Test-time / inference-time optimization (self-refine, reflection, tree search, process reward model–guided reasoning, etc.); - Tool use optimization (tool-use SFT, tool-use trajectory RL, tool selection optimization); - Multi-agent collaboration and deliberation; - Automated prompt / workflow optimization (e.g., DSPy, TextGrad — "gradient-style" optimization of prompts and workflows); - Agent distillation into smaller, more efficient models; - Agent evaluation and reward modeling (LLM-as-Judge, PRMs, Agent benchmark design, etc.); 3. And, based on team and business realities, judge which directions are worth investing in and translate them into team capabilities. 4. Track the latest Agent architectures from OpenAI, Anthropic, and others, and adapt them deeply to our creator business. 5. Partner deeply with the Algorithm team — understands the real needs of algorithm iteration, and ensures that algorithm infrastructure accelerates rather than bottlenecks algorithmic innovation. 6. Develop deep understanding of creators as a B2B user group; translate business insights into algorithm infrastructure decisions. Minimum Qualifications: 1. Deep understanding of the Agent technical stack — familiarity with the architectural approaches of frontier Agent practices such as OpenAI SDK and Claude Code, and a clear point of view on the capability boundaries and algorithmic challenges of Agents. 2. Hands-on experience with LangGraph (or equivalent frameworks) for building production-grade, domain-customized agents. 3. Systematic understanding of Agent optimization — familiarity with the foundational directions (Agent RL, hierarchical memory and Memory RL, SFT), plus hands-on practice or deep familiarity with at least 2-3 frontier directions (e.g., test-time optimization, tool-use optimization, multi-agent collaboration, automated prompt/workflow optimization, Agent distillation, Agent evaluation). 4. Familiarity with Agentic Search design and implementation, with a clear understanding of the paradigm shift from traditional retrieval to agent-driven retrieval. 5. Deep understanding of B2B / ToB businesses — able to reason about algorithm infrastructure from B2B-specific angles (user workflows, ROI, explainability, controllability) rather than directly applying consumer-product intuitions. 6. Technical judgment and forward-looking perspective — able to identify directions with real business value in a rapidly evolving Agent landscape, and bring fresh thinking to the team. 7. Outstanding cross-team collaboration skills — able to build a deep partnership with the product, algorithm and other cooperation engineer teams and drive complex algorithm projects to high-quality delivery. Preferred Qualifications: 1. End-to-end experience building LLM-powered (especially Agent) algorithm infrastructure from 0 to 1. 2. Deep hands-on experience with RL / RLHF / DPO / GRPO and other LLM alignment techniques. 3. Hands-on exploration or published work in test-time scaling, process reward models, or Agent self-improvement. 4. Practical experience with automated prompt / workflow optimization (DSPy, TextGrad, etc.). 5. Experience with algorithm infrastructure for content generation (copywriting, scripts, etc.). 6. Experience with B2B / SaaS / CRM businesses, either on the algorithm or infrastructure side. 7. Experience with international / multilingual / multi-region products.