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トレンド企業

トレンド企業

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求人TikTok

Staff Research Engineer, LLM - TikTok Ads Core ML, Ranking

TikTok

Staff Research Engineer, LLM - TikTok Ads Core ML, Ranking

TikTok

San Jose, CA

·

On-site

·

Full-time

·

2mo ago

報酬

$208,800 - $438,000

福利厚生

Learning

Equity

必須スキル

TypeScript

React

PostgreSQL

Responsibilities

Tik Tok Ads Core ML Team aims at creating automatic delivery products for the next generation and developing advertising as a business, instead of just a monetization tool to consolidate the delivery funnel framework allowing multiple teams to iterate parallel.

We're looking for innovative Staff Research Engineers to join our Core ML Ranking team. Ads Core Ranking team specifically focuses on maximizing delivery system efficiency and revenue growth through state-of-the-art models and frameworks. Our research topics include but not limited to: Generative Retrieval and Large Recommendation Model, LLM-based Ranking Application, and Optimization of System Resource Allocation with ROI target.

As part of our team, you will be responsible for:

  • Spearhead the development of a global advanced advertising delivery system by integrating cutting-edge technologies and research, including ML/DL, Reinforcement Learning, LLM, and scaling laws, within complex monetization scenarios.
  • Optimize efficiency across the entire advertising funnel, covering Recall & Rough-sort, Fine-sort (CTR/CVR), format/creative personalization, and system resource allocation.
  • Lead strategic initiatives and drive key projects by leveraging deep business acumen in monetization, ranking, search, and recommendation, ensuring precise technical decision-making.
  • Establish and refine system frameworks and standards, continuously enhancing efficiency to meet diverse vertical business needs.
  • Collaborate with product and business teams across global markets to maximize impact.

All of our team effort is to continuously pursue and establish a world-leading ranking model & framework that always benefits our collaborators, users and customers to get better returns.

Qualifications

Minimum Qualifications:

  • MS or above degree in Computer Science, Statistics, Operation Research, Applied Mathematics, Physics or similar quantitative fields, with related experience in any of the following domains: search, ranking or recommendation.
  • Hands-on experience in one or more of the following areas: machine learning, deep learning, statistical models and applied mathematical methods.
  • Solid programming skills, proficient in C/C++ and Python. Familiar with basic data structure and algorithms. Familiar with Linux development environment.
  • Familiarity with online experimentation and analytics.
  • Familiarity with big data systems including Hadoop and Spark.
  • Familiar with architecture and implementation of at least one mainstream machine learning programming framework (Tensor Flow/Pytorch/MXNet).
  • Curiosity towards new technologies and entrepreneurship.

Preferred Qualifications:

  • Experience in reinforcement learning, transfer learning, and counter-factual optimization is a plus.
  • Understanding of the business value of online advertising.
  • Paper publications in NLP/CV/recommender system domain areas (such as, paper listing, workshop, oral on Rec Sys/KDD/ICML/NeurIPs/CVPR, etc.).
  • Experience in LLM is a plus.

Compensation

The base salary range for this position in the selected city is $208,800 - $438,000 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

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.

Additional Information 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.

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スクラップ

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TikTokについて

TikTok

TikTok

Late Stage

A short-form video entertainment app and social network platform

10,001+

従業員数

Los Angeles

本社所在地

$220B

企業価値

レビュー

3.8

10件のレビュー

ワークライフバランス

2.8

報酬

3.7

企業文化

4.1

キャリア

3.2

経営陣

2.9

68%

友人に勧める

良い点

Great team dynamics and support

Innovative and creative culture

Good learning opportunities

改善点

Work-life balance challenges

Fast-paced and stressful environment

High expectations and tight deadlines

給与レンジ

49件のデータ

Mid/L4

Mid/L4 · 2D 3D Artist

1件のレポート

$195,000

年収総額

基本給

$150,000

ストック

-

ボーナス

-

$195,000

$195,000

面接体験

2件の面接

難易度

4.0

/ 5

期間

21-35週間

体験

ポジティブ 0%

普通 0%

ネガティブ 100%

面接プロセス

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Behavioral Interview

5

Final Round

6

Offer

よくある質問

Coding/Algorithm

Behavioral/STAR

Technical Knowledge

Culture Fit