Staff Machine Learning Engineer

We serve several hundred million ads daily to our users, sourcing them from both our direct demand and a network of partners integrated via ORTB channels or GAM. Delivering ads at this scale to a diverse user base requires deep neural network (DNN)-based recommendation models, along with a robust infrastructure to support seamless ad delivery.

Beyond recommendation models, our system incorporates a sophisticated marketplace designed to maximize value for advertisers while ensuring optimal budget consumption at a predictable pace. Additionally, it upholds a fair and competitive environment for all partners by ensuring equitable auctions for each ad slot.

Some of the key challenges our team addresses include:

• Developing personalized ad recommendation models.
• Building world-class infrastructure to deliver ads in near real-time.
• Maintaining a marketplace that ensures fair value delivery to advertisers while optimizing budget utilization.
• Ensuring a level playing field for all partners, whether integrated directly or through networks.

Beyond this, there are exciting opportunities to deploy ML in the ADs ecosystem like on the network ADs side of things as well as on balancing ad revenue with regression in user experience.

This is a high-impact role with direct revenue implications, where experimentation success rates are significantly higher due to our dynamic and fast-paced environment.

You would be joining us at an exciting time! The science behind recommendation systems is rapidly changing, and were making big progress at a rapid pace.

Who are you?

1. Design and help develop systems that serve recommendations to over 300 million users
2. Drive ML roadmap creation and execution, specifically around Ads
3. Provide technical guidance in ML model formulation, implementation & experimentation, and take end to end ownership of ML systems, and key user satisfaction based metrics
4. Drive architectural strategy and design for complex ML systems that support the needs of users, creators and content stakeholders

Preferred Qualifications

• Hands-on experience training and serving large-scale models using frameworks such as Tensorflow or PyTorch
• Experience productionising machine learning models, and managing and designing end to end ML systems, and data pipelines
• Deep understanding of the mathematical foundations of Machine Learning algorithms
• Direct experience in building and applying large-scale (100M+ users) machine learning solutions for feed ranking, and personalizing recommendations.
• You stay up-to-date with the state-of-the-art technology in the domains of recommender systems, data engineering, and machine learning. Relevant publications in top tier applied machine learning conferences is a plus
• You have a Masters or PhD in ML, statistics, or an engineering field with 5+ years of experience