Knorex

Tóm tắt công việc

Knorex develops a cloud-based, highly scalable, all-in-one advertising and marketing platform called Knorex XPO™ () where Advertisers, Agencies and Publishers can easily create, optimize & publish Dynamic Ads worldwide with speed and ease. As part of the platform offering, anyone can traffic their Dynamic Ads programmatically via automated buying through our in-house real-time bidder and optimizer.

Real-time bidding (RTB) is a means by which advertising inventory is bought and sold on a per-impression basis, via programmatic instantaneous auction, similar to financial markets. The entire process generally takes place in less than 100 ms. Owing to the large and real-time stream of data, coming up with efficient and pragmatic solutions and algorithms to the challenging problems will become imperative. At Knorex R&D, we apply large-scale machine learning (ML) models to optimize the return-on-investment of our advertisers’ budget. Tasks we work on include user interaction prediction, bidding optimization, contextual targeting, etc. We are seeking for Machine Learning Engineers with the zest and passion to work on our in-house machine learning platform, which covers the whole life-cycle of machine learning, from data collection and augmentation pipeline, to model training and serving, to monitoring and analysis. You will be joining our R&D team working closely with Data Scientists, Research Scientists and Software Engineers to build a highly scalable system that can handle billions of requests per day, all delivered in milliseconds.

In this role, you will be expected to possess solid experience working on large-scale and low-latency ML systems. You need to acquire a deep technical understanding of the platform, work with our cross-country team located regionally to learn about the business and technical analytics requirements and translate them into production system.

Key Responsibilities

• Plan, design and develop components in the data pipeline to enable various machine learning models in production.
• Work closely with Data Scientists and Research Scientists to translate the state-of-the-art to production systems.
• Develop metrics to measure the outcome/impact of your introduced solutions.
• Investigate the existing pipeline, identify bottlenecks and optimize the throughput and latency of ML components.
• Document and improve the solutions over time.
• Evaluate and identify new technologies for implementation.
• Communicate with our business and technical teams to understand the analytics requirements.
• Respond and follow up to incorporate feedback and draw new insights.
• Prioritize tasks to meet multiple deadlines.

What you can expect from us:

Your Skills and Experience