Responsibilities Responsibilities: - Research and develop a cutting-edge global advertising delivery system using advanced technologies, including ML/DL, RL, LLM, and scaling laws in ad recommendation. - Optimize efficiency across the entire advertising funnel, focusing on Recall & Rough-sort, Fine-sort (CTR/CVR), format/creative personalization, and system resource allocation. - Design and establish system frameworks and standards to continuously enhance modeling efficiency. - Build and manage the delivery pipeline, encompassing modeling, bidding, traffic strategy, and optimizing organic video recommendation systems to meet diverse vertical business needs. - Drive the influence of user experience and revenue targets, establishing the team as a core pillar within the organization. Qualifications Minimum Qualifications: - Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline. - Solid programming skills, proficient in C/C++ and Python. Familiar with basic data structure and algorithms. Familiar with Linux development environment. - Good theoretical grounding in machine learning/deep learning/algorithm concepts and techniques. - Familiar with architecture and implementation of at least one mainstream machine learning programming framework (TensorFlow/Pytorch/MXNet), familiar with its architecture and implementation mechanism. - Good analytical thinking and critical thinking capabilities. Preferred Qualifications: - Participation in national math/coding competitions (e.g., ACM, Hacker Cup, Hash Code, USACO, IOI, CCPC, etc.). - Strong understanding of key domains in online advertising systems, including ads bidding & auction, ads quality control, and related concepts such as CPC/CPM, CTR/CVR, Ranking/Targeting, Conversion/Budget, Campaign/Creative, Demand/Inventory, DSP/RTB. - Experience with resource management and task scheduling in large-scale distributed software environments (e.g., Spark, TensorFlow). - Published papers/citations or conducted paper reviews in areas such as NLP, CV, and Recommender Systems (e.g., RecSys, KDD, ICML, CVPR, NeurIPS). - Familiarity with advanced techniques like LLM, reinforcement learning, transfer learning, and counterfactual optimization is a plus. Job Information [For Pay Transparency]Compensation Description (Annually) The base salary range for this position in the selected city is $162000 - $316800 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 3. Exercising sound judgment.