The Global E-Commerce Affiliate Algorithm team is dedicated to building a thriving and efficient content-driven e-commerce supply ecosystem, leveraging technology to drive continuous GMV growth. Our core focus areas include the creator growth system, intelligent creator-product search and recommendation engines, and AI-native content creation tools for creators. Centering on creator product selection and business matchmaking, we build unified search and recommendation capabilities that power diverse distribution scenarios—including the Affiliate Product Marketplace, Affiliate Creator Marketplace, and Search & Recommendation—substantially boosting matching and transaction efficiency between creators and merchants. Simultaneously, by leveraging lead mining and creator growth task frameworks, we continuously expand the base of high-quality creators to enhance the depth and diversity of content supply. In terms of product innovation, we build AI-native workflows that provide creators with smart product sourcing, script generation, and content optimization capabilities—empower creators to achieve more efficient content production and sustainable business growth.
We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).
Job Responsibilities
Build and evolve the agent runtime (harness / agent loop) powering our creator / seller agents — orchestrate skills, tools, and context.
Engineer context & memory for long multi-turn agents — agentic memory (structured note-taking), context compaction / summarization, context editing / observation masking, and just-in-time (retrieve-then-load) retrieval.
Post-train and fine-tune LLMs (SFT / DPO / RL) and build the data flywheel that turns served conversations into training / eval / retrieval signals.
Design and integrate tools, Skills, and MCP connectors (tools-as-APIs), plus skill / tool search for large tool inventories.
Build evaluation — LLM-as-judge with human-agreement calibration; regression / safety / cost / latency-aware harnesses; close the offline↔online gap.
Build the self-evolving loop — replay + task/environment simulator + Auto-RCA / Auto-GSB / Auto-Policy — so the system continuously improves itself.
Own one high-leverage end-to-end surface and ship it to production across 30+ languages, measured on real business metrics (GMV, Content, Creator/Merchant satisfaction).
Qualifications
Minimum Qualifications - Currently Pursuing a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline. - Strong Python plus one of C++ / Go / Rust / Java - Solid ML / DL / NLP fundamentals, with genuine hands-on experience with LLMs or agents (coursework, research, internship, competition, open-source, or a serious side project) - Able to read a paper or an engineering blog and turn it into working code.
Preferred Qualifications
Have built the runtime, not just called an API — even at research / hobby / competition scale: your own agent loop / harness, a memory / context-management system, a RAG or tool-use agent, or a fine-tuned / post-trained model
Hands-on experience in any one of: post-training (SFT / DPO / RLHF / RLAIF / RLVR, reward modeling); agent systems (harness, context engineering, MCP / Skills, sub-agents, tool search); evaluation (LLM-as-judge, τ-bench / SWE-bench / GAIA / BFCL); inference & serving (vLLM / TensorRT-LLM, MoE, KV / prompt caching); or multilingual NLP — depth in one is enough, breadth welcome
Publications, strong competition results (ACM-ICPC / Kaggle / ML competitions), or notable open-source contributions
