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机器学习工程师 - 搜索广告

雇主

TikTok

地点

California · San Jose · 美国

待遇

$面议

工作模式

线下

截止日期

1月8日

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岗位摘要

Join the Search Ads team to develop and optimize large-scale machine learning models for search adve

岗位职责

The Search Ads team constantly pushes the boundaries of general search engine monetization across our apps, including TikTok, TopBuzz, BuzzVideo, and more, building a globally leading Search Ads monetization system.
At the Search Ads team, you will have the chance to work on large-scale distributed storage and architecture, NLP, Rank, and IR related problems.
You will be also deeply involved in the innovation and optimization of our Ad format, creative display, and the ROI of ads delivery.
We are looking for candidates who brave difficulties, share a passion for tackling complexity and developing our Search Ads product from 0 to 1 with a world-class team of passionate engineers.
What You'll Do:
• Participate in the development of a large-scale Ads system
• Responsible for relevance model and strategy optimization, such as semantic matching models, active learning, text/photo/video multi-model, ranking strategy, etc
• Participate in the development and iteration of Ads algorithms by using Machine Learning.
• Work on NLP (Natural Language Processing) capability improvement and query understanding, such as query classification, seq2seq, NER (Named Entity Recognition), knowledge graph, bidword optimization, etc
• Work on CTR/CVR model estimation accuracy, data analysis, modeling, feature engineering
• Research and develop Ads pacing algorithms, ads traffic control, etc
• Partner with product managers and product strategy & operation team to define product strategy and features

申请条件

Candidates who brave difficulties, share a passion for tackling complexity and developing our Search Ads product from 0 to 1 with a world-class team of passionate engineers.

雇主简介

TikTok is a leading short-form video platform that develops core training and serving infrastructure for large-scale recommendation systems and AI infrastructure for foundation models and LLMs.

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数据来源:TikTok

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