We are a fast-growing technology company redefining digital advertising with innovative, data-driven solutions. Our mission is to empower businesses with a high-performance ad platform that drives exceptional results.

About this role

As a Machine Learning Engineer specializing in computational advertising, you will design algorithms for bidding strategies, real-time traffic control, and value estimation. Your work will directly impact ad revenue growth through advanced ML applications in our advertising ecosystem.

Key Responsibilities

1. Algorithm Design & Optimization

• Develop intelligent bidding algorithms (e.g., automated bid shading, ROI-based optimization)

• Design real-time traffic control and calibration algorithms to stabilize platform economics

• Architect auction mechanisms to balance user experience and advertiser value

2. Prediction Modeling

Build high-accuracy models for the Traffic cost estimation, User lifetime value prediction, Deep conversion event probability (e.g., purchases, app installs)and Implement multi-objective optimization for competing metrics

3. Research & Innovation

• Apply state-of-the-art ML techniques (causal inference, bandit learning) to ad delivery challenges

• Explore LLM applications for creative optimization and audience targeting

• Publish novel solutions at top-tier conferences (KDD, WWW, etc.)

Qualifications

Must-Have:

• Master's/PhD in CS, EE, Automation, or related quantitative fields

• Strong analytical skills with ability to abstract business problems into ML models

• Expertise in Machine learning (especially GBDT, DNN, reinforcement learning), Operations research/control theory, Computational advertising principlesand Production-level coding ability (Python/Scala/C++)

Preferred

2+ years experience in:

• DSP/SSP platforms

• Recommendation/search ranking systems

• Large-scale distributed ML training

• Publications in ML/AdTech domains