Machine Learning Engineer

Quantcast · US · 5d ago

Hybrid Mid-Level JavaPython

About the role

At Quantcast, we don't just build advertising technology, we revolutionize how it works. Our AI-powered Demand Side Platform (DSP) connects the world's most ambitious marketers with their ideal audiences across the open internet, delivering results that actually move the needle. Since 2006, we've been the industry's trailblazer, launching the first AI-powered measurement platform for publishers and the first AI-driven DSP. Our AI doesn't just optimize—it delivers the measurable outcomes that matter most to our clients, giving them the competitive edge they need in a crowded marketplace. Ready to join the team that's defining the future of digital advertising?

The Modeling team is responsible for Machine Learning (ML) systems at Quantcast. We build and maintain multiple ML products that price millions of bid requests per second in a real-time auction environment to maximize advertiser outcomes. For each bid our models predict age, gender, viewability, fraud, advertiser relevance and many more characteristics. Using NLP, clustering and LLMs we build topics in multiple languages to help our advertisers target customers interested in relevant content.

As a Machine Learning Engineer you care about the health and maintainability of our systems and the velocity of the engineering teams. You explore data, research new algorithms, experiment with proof of concepts, and build out scalable real-time production systems to tackle challenges the company faces.

What you'll do:

Design, code, test, and debug ML applications and constantly improve large-scale global systems that respond to millions of real-time requests per second efficiently.

Run Machine Learning experiments to test new modeling ideas.

Write clean, efficient, and maintainable code using industry best practices.

Collaborate closely with engineering teams, to deliver high-quality ML products. Participate in code reviews and provide constructive feedback to team members.

Identify performance bottlenecks and optimize system components for enhanced scalability.

Keep up to date with developments in machine learning outside the company.

Who you are:

New Grad to 1 year of experience.

You must be work-authorized in the United States without the need for employer sponsorship.

This is a hybrid role based in our San Francisco office. To ensure a manageable commute for in-office days, candidates must reside within a 60-mile radius of San Francisco, CA. No relocation candidates at this time.

Fluency in Python, Java or similar programming languages.

Strong skills in mathematics and statistics.

Working knowledge of ML algorithms such as classification, control systems, optimization, clustering, LLMs or recommendation systems.

An interest in distributed system and software design, concurrent algorithms, data structures, and software engineering.