国内当前最热的互联网公司在美西开设的分公司。 招聘人工智能，机器学习方面工程师。
对英文要求不高， 华人有更大上升空间， 待遇极佳， 无明确工作年限要求。
推荐朋友成功，也有 $1000推荐费哦。

发送简历： tracynilai@gmail.com
电话咨询： 3028983597

Title: Applied Machine Learning Engineer
Location: Palo Alto, CA/ Seattle
Job Description
ByteDance is a technology company operating a range of content platforms that inform, educate,

entertain and inspire people across languages, cultures and geographies. Our groundbreaking AI-
enabled platform cuts through the noise in an increasingly complex landscape by detecting,

classifying, and determining the significance of public information. Our culture promotes cross team
interaction, work-life balance and the sharing of information and ideas because it enables us to do our
best work and have fun.

1. Participate in the development of a large-scale recommendation system and machine learning
platform;
2. Participate in the development and iteration of recommendation algorithms.
3. Improve core recommendation algorithm;
4. Conduct cutting-edge research in recommendation related problems, and apply the technology to
different business scenarios.
5. Ship production quality code for the offline model building and work with engineering team to
develop/deploy the run time system for the model.
6. Analyze software performance problems and implement optimizations.
7. Active contribution to identify areas of improvement in personalization and recommendation
products. Ability to adapt latest in literature in the area to build efficient and scalable models.
Requirements
1. Solid programming skills, proficient in C/C , good programming style and work habits;
2. Familiar with at least one mainstream deep learning programming framework
(TensorFlow/Caffe/MXNet), familiar with its architecture and implementation mechanism;
3. Familiar with deep learning algorithms (CNN/RNN/LSTM, etc.);
4. Ability to solve problems independently, good sense of teamwork and communication skills;
5. Has experience with open sourced deep learning framework.
Preferred qualifications:
1. Familiar with the parallelization of Sparse LR, FFM, and deep models on large-scale data;