Hiring: Tech Lead / Principal Engineer, Creator Agent Algorithm Infrastructure Company: ByteDance Location: Seattle, WA Job Posted Time: 2026-09-11 18:05:17 Target Skills & Keywords : LLM, Machine Learning, OpenAPI About the job Required Skills: •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: •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). •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.); Qualifications: •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 p Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!