Software Engineer - Global E-Commerce Search Infrastructure (TikTok Shop)

Responsibilities

We're building the next-generation AI search and shopping assistant for TikTok Shop, TikTok's global commerce platform, - powering Q&A cards, in-app chatbot, and visual search experiences that help billions of users discover products, explore stores, and shop through natural conversation. Our team owns the full search stack: from retrieval and ranking to multi-agent LLM engines, post-training infrastructure, and personalized memory. We translate cutting-edge research into production systems at global scale, with a focus on relevance, latency, and fast algorithm iteration.

Responsibilities:
- Build AI Search Agents: Design and ship ReAct-based agents with planning, memory, and tool use; implement DAG workflows and RAG pipelines for multi-turn shopping assistance and query understanding. Own the unified Agent Harness across Q&A cards, in-app chatbot, and visual search surfaces - with MCP tool-chain integration and end-to-end A/B support.
- Improve LLM Query Understanding: Drive multi-turn conversation, cross-lingual analysis, and LLM reasoning chains for accurate, trustworthy search results; optimize answer generation pipelines (quantization, KV cache, continuous batching) for quality and latency.
- Build Personalized Memory Infrastructure: Design high-throughput storage and retrieval for long-term user profiles and real-time memory (MemAgent); build near-real-time feature pipelines over billion-scale behavior sequences for low-latency serving of personalized signals.
- Contribute to Training and Inference Infrastructure: Collaborate on post-training pipelines (SFT, RL, distillation) and model-serving infrastructure (tensor parallelism, speculative decoding, PD separation) to accelerate experimentation and hit latency targets.
- Ship Research to Production: Bridge research and engineering - partner with algorithm teams to evaluate agent and LLM innovations, accelerate adoption, and ensure new capabilities land stably in production at scale.

Qualifications

Minimum Qualification(s):
- Bachelor's or Master's in Computer Science, Computer Engineering, or a related technical field.
- At least 3 years of industry experience building large-scale distributed systems, search infrastructure, or low-latency online services.
- Proficiency in C++, Go, or Java (C++ preferred); strong systems fundamentals - data structures, OS, networking, multithreading, and Linux performance tuning.
- Solid grasp of LLM and agent technologies - RAG, tool use, and multi-turn reasoning - with a track record of contributing to production AI systems.
- Excellent system design instincts; able to independently architect and ship reliable, high-performance services; strong communication and ownership.

Preferred Qualification(s):
- Background in large-scale search, recommendation, advertising, or personalization systems - particularly e-commerce search at 100M+ user scale.
- Experience shipping agentic systems or RAG pipelines in production - ReAct, tool calling, DAG orchestration, or MCP integrations.
- Familiarity with LLM inference optimization - quantization, KV cache, speculative decoding, tensor parallelism - or hands-on experience with vLLM, TensorRT-LLM, or SGLang.
- Experience with post-training workflows: SFT, RL (RLHF / PPO / GRPO), distillation, or reward model design.
- Research publications at NeurIPS, ICML, ACL, CVPR, RecSys, or OSDI.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $153900 - $300960 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.

Responsibilities

Responsibilities

We're building the next-generation AI search and shopping assistant for TikTok Shop, TikTok's global commerce platform, - powering Q&A cards, in-app chatbot, and visual search experiences that help billions of users discover products, explore stores, and shop through natural conversation. Our team owns the full search stack: from retrieval and ranking to multi-agent LLM engines, post-training infrastructure, and personalized memory. We translate cutting-edge research into production systems at global scale, with a focus on relevance, latency, and fast algorithm iteration.

Responsibilities:
- Build AI Search Agents: Design and ship ReAct-based agents with planning, memory, and tool use; implement DAG workflows and RAG pipelines for multi-turn shopping assistance and query understanding. Own the unified Agent Harness across Q&A cards, in-app chatbot, and visual search surfaces - with MCP tool-chain integration and end-to-end A/B support.
- Improve LLM Query Understanding: Drive multi-turn conversation, cross-lingual analysis, and LLM reasoning chains for accurate, trustworthy search results; optimize answer generation pipelines (quantization, KV cache, continuous batching) for quality and latency.
- Build Personalized Memory Infrastructure: Design high-throughput storage and retrieval for long-term user profiles and real-time memory (MemAgent); build near-real-time feature pipelines over billion-scale behavior sequences for low-latency serving of personalized signals.
- Contribute to Training and Inference Infrastructure: Collaborate on post-training pipelines (SFT, RL, distillation) and model-serving infrastructure (tensor parallelism, speculative decoding, PD separation) to accelerate experimentation and hit latency targets.
- Ship Research to Production: Bridge research and engineering - partner with algorithm teams to evaluate agent and LLM innovations, accelerate adoption, and ensure new capabilities land stably in production at scale.

Qualifications

Qualifications

Minimum Qualification(s):
- Bachelor's or Master's in Computer Science, Computer Engineering, or a related technical field.
- At least 3 years of industry experience building large-scale distributed systems, search infrastructure, or low-latency online services.
- Proficiency in C++, Go, or Java (C++ preferred); strong systems fundamentals - data structures, OS, networking, multithreading, and Linux performance tuning.
- Solid grasp of LLM and agent technologies - RAG, tool use, and multi-turn reasoning - with a track record of contributing to production AI systems.
- Excellent system design instincts; able to independently architect and ship reliable, high-performance services; strong communication and ownership.

Preferred Qualification(s):
- Background in large-scale search, recommendation, advertising, or personalization systems - particularly e-commerce search at 100M+ user scale.
- Experience shipping agentic systems or RAG pipelines in production - ReAct, tool calling, DAG orchestration, or MCP integrations.
- Familiarity with LLM inference optimization - quantization, KV cache, speculative decoding, tensor parallelism - or hands-on experience with vLLM, TensorRT-LLM, or SGLang.
- Experience with post-training workflows: SFT, RL (RLHF / PPO / GRPO), distillation, or reward model design.
- Research publications at NeurIPS, ICML, ACL, CVPR, RecSys, or OSDI.