Between Heaven and Earth: Reflections of an Algorithm Engineer

In this personal essay, the author reflects on the current state of algorithm engineering, noting that the industry’s low‑hanging fruit has been exhausted and that obsessing over marginal AUC or CTR gains with new deep‑learning structures has become both boring and demotivating.

The author challenges the misconception that algorithm engineers are synonymous with deep‑learning engineers, pointing out that many advertising and recommendation problems are better solved with traditional algorithms, control theory, or game‑theoretic approaches rather than deep models.

He describes the "sky" of an algorithm engineer as a solid understanding of the overall system architecture, illustrating how isolated model improvements can fail without alignment to the broader system, and sharing anecdotes such as a failed PID‑based pacing algorithm that crashed the system.

The "earth" is portrayed as meticulous attention to data details; the author stresses that clean, well‑covered, and properly scaled features are the foundation for any model’s success, and warns against superficial feature additions without proper analysis.

To solve real‑world problems, the author proposes three focus areas: (1) sample and feature quality, (2) label definition and handling, and (3) model adaptation to business specifics, urging engineers to think critically rather than blindly transplanting existing models.

Overall, the piece encourages algorithm engineers to balance high‑level system thinking with low‑level data scrutiny, leveraging a mix of machine‑learning and classic algorithmic techniques to achieve robust, scalable solutions.