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 E-commerce 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).
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   • 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.);
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     • And, based on team and business realities, judge which directions are worth investing in and translate them into team capabilities.
     • Track the latest Agent architectures from OpenAI, Anthropic, and others, and adapt them deeply to our creator business.
     • Partner deeply with the Algorithm team - understands the real needs of algorithm iteration, and ensures that algorithm infrastructure accelerates rather than bottlenecks algorithmic innovation.
     • Develop deep understanding of creators as a B2B user group; translate business insights into algorithm infrastructure decisions.Minimum Qualifications:
     • 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.
     • Hands-on experience with LangGraph (or equivalent frameworks) for building production-grade, domain-customized agents.
     • 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).
     • Familiarity with Agentic Search design and implementation, with a clear understanding of the paradigm shift from traditional retrieval to agent-driven retrieval.
     • 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.
     • 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.
     • 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.