Cyberlab

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