Machine Learning Engineer - Local Services Search (ByteDance Singapore)

About the team

We are the TikTok Local Service Search team, building search algorithms that power both general search and the local services vertical, connecting user needs with places (POIs), products, and videos. Our users discover restaurants, hotels, and destinations through video content, then turn to search to learn more, compare options, and make decisions. We combine the understanding, generation, and reasoning capabilities of large language models with search and personalization technology, aiming to improve information access, support purchase decisions, and drive transaction efficiency throughout this journey.

What You'll Do

Depending on your experience, you will focus on one or more of the following areas:

1. POI Search & NLP Infrastructure Own query understanding, content understanding, relevance, and POI retrieval, ranking, and result presentation. Leverage LLMs to understand multilingual expressions of places, categories, budgets, and search intents, and extract structured information from videos and location data to power search. Combine semantic retrieval, geolocation signals, and click/conversion prediction to improve the accuracy and relevance of restaurant, hotel, and other search results. Apply these capabilities to the online search pipeline through LLM-based labeling, training data development, model fine-tuning, and distillation, and explore approaches such as generative retrieval.
2. Video Ranking & Personalization Own retrieval, ranking, and personalization modeling for local services video search. Use LLMs to interpret the places, services, and consumption experiences featured in videos, assess their relevance and information value to user queries, and feed these insights into training data and ranking models. Optimize multiple objectives — including clicks, content consumption, and transaction conversion — using user interests, location, and behavioral sequences, so users can more easily find videos that are relevant to their immediate needs and rich in useful information.
3. AI Search & Local Services Agent Build agentic search experiences for restaurant, hotel, and travel needs. Combine LLMs, retrieval-augmented generation (RAG), and multimodal understanding to organize evidence-based answers from videos, location data, and user experiences, helping users compare options. Explore multi-turn intent clarification, context and preference memory, and tool use, enabling users to progressively refine their searches by location, budget, party size, and more. Contribute to model training, effect evaluation, and the rollout of search products.
4. Algorithm Delivery & Optimization Drive data infrastructure, model training, offline evaluation, online serving, and A/B experimentation. Continuously improve search quality and business outcomes based on user feedback, while balancing model performance, response latency, and computational cost.

Qualifications

Minimum Qualifications

1. Solid foundation in machine learning and deep learning, with deep understanding or hands-on experience in at least one of the following: natural language processing, information retrieval, personalization modeling, or LLM applications.
2. Strong coding skills and a solid grasp of data structures and algorithms; familiar with Linux development environments and able to develop and run experiments in C++ and Python.
3. Strong analytical and problem-solving skills — able to devise solutions from user needs and data, validate them through offline evaluation and online experiments, and drive them to real-world impact.

Preferred Qualifications

1. Experience in search, recommendation, or advertising algorithms, or in local services / e-commerce businesses; experience in query understanding, relevance, retrieval, ranking, or CTR/CVR modeling.
2. Experience in LLM fine-tuning and distillation, multimodal understanding, generative retrieval, RAG, agent development, or model evaluation. Candidates with strong expertise in any of these directions who are eager to explore the intersection of LLMs and search are highly welcome.