Staff Machine Learning Engineer, User Understanding

Unity Technologies•San Francisco, CA

About The Position

At Unity, we’re committed to building a culture that fosters collaboration and innovation. Within our fast-paced environment, we’re tackling complex challenges that drive meaningful impact for creators and users across our ecosystem. Our Foundation Modeling team plays a central role in this effort by developing state-of-the-art conversion rate (CVR) prediction and market price models that power our ad ranking and recommendation systems. By leveraging large-scale data and contextual signals, we continuously enhance prediction relevance and accuracy. This drives outcomes such as higher user engagement, stronger conversion performance, and better return on ad spend—helping advertisers achieve their goals while delivering a more engaging experience for users. We are seeking a highly skilled Staff ML Engineer to build and maintain advanced systems for understanding user behavior across applications. You will be responsible for improving ad ranking, conversion predictions, and recommendation systems, enabling richer behavioral models, smarter bidding strategies, and more effective ad performance.

Requirements

Advanced degree (MS or Ph.D.) in Computer Science, Machine Learning, Statistics, related field, or equivalent practical experience.
5+ years of hands-on experience building and operating large-scale Ads delivery and optimization systems.
3+ years of experience in content understanding, behavior modeling, user profiling, and cohort analysis.
Significant experience in one or more general-purpose programming languages like Python, Go, Scala, C++ or similar.
Knowledge of metric design, experimentation methodologies, and large-scale data analysis.

Nice To Haves

Experience designing and productionizing optimization algorithms in an ad serving platform and/or other marketplaces.
Experience with state-of-the-art control systems, reinforcement learning algorithms.

Responsibilities

Design, implement, and maintain user understanding models.
Ensure model quality and reliability by continuously monitoring performance, calibrating predictions, and addressing data drift or delayed feedback.
Actively participate and work with other leads to set the long-term direction for the team, plan and oversee engineering designs and project execution.
Effectively communicate complex technical concepts to non-engineering stakeholders.