Principal Algorithm Engineer

Key Responsibilities:

• Model Development & Tuning: Design, train, and optimize core ad-ranking models (CTR, CVR, CPA) and implement smart bidding and cold-start strategies.
• End-to-End Pipeline Delivery: Build and maintain offline data pipelines, feature engineering workflows, and online low-latency inference services.
• Experimentation & A/B Testing: Design and execute A/B tests, analyze experiment metrics, and rapidly iterate on model iterations based on performance feedback.
• System Efficiency & Stability: Partner with engineering teams to optimize inference latency, throughput, and model-serving reliability under high-concurrency traffic.

• Technology Exploration: Track and apply state-of-the-art machine learning techniques (e.g., multi-task learning, sequential models) to solve real-world advertising challenges.

Qualifications:

• Education: Bachelor’s degree or above in Computer Science, Data Science, Statistics, Mathematics, or a related technical field.
• Core Skills: Strong foundation in machine learning and deep learning, with a clear understanding of ranking/recommendation architectures (e.g., DeepFM, DIN, MMoE).
• Data Processing: Familiarity with big data tools (Spark, Flink, Hive) and strong SQL skills for large-scale data manipulation and feature engineering.
• Bonus Qualifications: Prior experience in ad tech, search, or recommendation systems, or experience optimizing high-throughput, low-latency online serving systems.