Manager, Machine Learning

Viant Technology•Los Angeles, CA

9d•$190,000 - $220,000

About The Position

The Machine Learning team at Viant is revolutionizing the Ad Tech industry with innovative machine learning systems. By automating manual processes in creating, launching, and measuring digital ads, we build autonomous systems that process hundreds of millions of events daily. We are seeking a hands-on Machine Learning Manager who will lead a team of engineers in building, deploying, and improving machine learning solutions that directly drive business impact. In this role, you will contribute technically while developing a high-performance team culture, ensuring strong engineering standards, and delivering reliable ML systems that solve complex, high-value problems.

Requirements

4+ years of experience in machine learning, with at least 1+ years leading and scaling high-performing teams.
Bachelor’s degree in Computer Science, Engineering, or a related field; Master’s degree preferred.
Strong expertise in developing and applying machine learning models for real-world business applications.
Proven ability to deliver results at scale through innovation and operational excellence.
Exceptional problem-solving skills and the ability to tackle challenges not previously solved in the industry.

Nice To Haves

PhD in Machine Learning, Computer Science, or a related field.
Experience in the Ad Tech industry or with digital advertising systems.
Publications or contributions to leading conferences in machine learning or data science.

Responsibilities

Provide day-to-day technical leadership for the Machine Learning team, ensuring high-quality execution and alignment with company goals.
Lead the end-to-end development, deployment, monitoring, and maintenance of machine learning models that power Viant’s products and internal systems.
Coach, mentor, and grow machine learning engineers and scientists, fostering excellence in machine learning engineering practices, architecture, and operational rigor.
Apply advanced ML techniques—including supervised learning, causal inference, forecasting, and personalization—to solve problems such as supply prediction, campaign performance modeling, and incremental value measurement.
Partner closely with Product and Engineering stakeholders to scope work, deliver actionable insights, and ensure successful integration of ML solutions.
Drive continuous innovation by exploring and adopting new modeling methods, tools, and approaches that elevate system performance and reliability.
Take on direct hands-on work—including model building, analysis, and MLE tasks—while actively contributing to the team’s roadmap and delivering key initiatives.