AI Agent Algorithm Engineer Intern (Global E-Commerce, Conversational AI) - 2027 Start (PhD)

About TikTok

We build the next-generation unified Agent system for TikTok's global e-commerce customer service — running in 30+ languages across one of the largest e-commerce surfaces on the internet.

Our north star is a self-evolving Agent, where post-training, harness, memory / context engineering, tools, and evaluation form one closed loop, and every served conversation becomes the next iteration's training / eval / retrieval / skill-induction signal.

We build the agent runtime itself — Codex / Claude-Code-class — not prompts on top of a vendor API.

As a new grad, you'll own a real end-to-end piece from day one and ship it to production.

Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.

Responsibilities

• Build and evolve the agent runtime (harness / agent loop) powering our customer-service agents — orchestrate skills, tools, and context; implement loop control & intervention, progressive disclosure, and behavior-level guardrails.
• 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 + user-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 (CSAT, resolution / containment rate).

Requirements

• 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

• 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

Application details

• 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).