Machine Learning Engineer Graduate (Lead Ads)

Tiktok Inc
San Jose, CA, United States
1 day ago
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Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Experience level
Starter
Compensation
$128,000.0 - $256,000.0
Working hours
Regular working hours

Tech stack

Mxnet Data Analysis C++ (Programming Language) Computer Engineering Graph Database Python (Programming Language) Machine Learning Natural Language Processing Named Entity Recognition Tensorflow Feature Engineering Pytorch
+5 more
Deep Learning Linux Development Information Technology Recurrent Neural Networks Golang

Job description

The Lead Ads team empowers TikTok’s global monetization (billion-dollar business) via efficiently delivering lead ads on TikTok. Our mission is to push the boundaries of large-scale ads delivery systems and lead the innovations of TikTok’s personalized online advertising., 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

Requirements

Individuals who are completing or have recently completed a Bachelor’s or above degree in Computer Science, Computer Engineering, or a related technical discipline.

  • Solid programming skills, including but not limited to: Go, C/C++, Python. Familiar with basic data structure and algorithms. Familiar with Linux development environment.
  • Good analytical thinking capability. Have essential knowledge and skills in statistics.
  • Good theoretical grounding in the machine and deep learning concepts and techniques (CNN/RNN/LSTM, etc.).
  • Familiar with the architecture and implementation of at least one mainstream machine learning programming framework (TensorFlow/PyTorch/MXNet), familiar with its architecture and implementation mechanism.

Preferred Qualifications:

  • Ads system, recommendation, searching, ranking, etc.

Benefits & conditions

The base salary range for this position in the selected city is $128000 - $256000 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

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