About Us**
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.
Job Overview**
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:
- Traffic cost estimation
- User lifetime value prediction
- Deep conversion event probability (e.g., purchases, app installs)
- 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:
- Degree 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 principles
- 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
