Director, Machine Learning - User Value

Unity Technologies SF•Mountain View, CA

About The Position

The opportunity Unity Vector represents the next generation of our AI-powered advertising ecosystem—a forward-looking evolution of our machine learning infrastructure and models. We are seeking a visionary Director of Machine Learning to lead our User Value team, a group at the heart of Unity's ambitious machine learning modeling initiatives. As a leader, you will bridge the gap between complex machine learning paradigms and core product strategy. You will manage and scale a high-performing organization of scientists and ML engineers, ensuring our models translate into high-value user acquisition for advertisers.

Requirements

Proven Leadership: 8+ years of experience in machine learning and software engineering, with 4+ years of experience managing teams
Domain Expertise: A strong track record of shipping ML systems at scale, specifically in areas like personalization, recommendation system, information retrieval, or behavioral modeling.
Product-Minded Engineering: A deep understanding of how technical ML metrics (e.g., accuracy, NDCG, precision-recall) connect to macro user and business metrics (e.g., retention, lifetime value).
Strategic Communication: Ability to articulate complex technical concepts clearly to non-technical executives and cross-functional partners.
Technical Foundations: An advanced degree (MS or PhD) in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience.
Sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.

Responsibilities

Shape the ML Vision: Define 1-3 year technical roadmap for user value modeling, aligning algorithmic capabilities with product and business growth goals.
Lead and Scale Teams: Mentor, recruit, and retain a team of applied scientists and machine learning engineers. Foster a culture of technical excellence, continuous learning, and rapid experimentation.
Drive Product Impact: Partner closely with leadership in Product, Design, and Data Science to identify new opportunities where ML can solve customer pain points and unlock tangible value.
Advance the Technical Stack: Oversee the development, deployment, and maintenance of scalable ML systems—spanning personalization, content recommendation, multi-task learning, reinforcement learning, and large language model (LLM) integration—ensuring high availability and low latency.
Balance Innovation and Execution: Establish rigorous frameworks for A/B testing, model evaluation, and guardrails to ensure algorithmic changes measurably improve customer trust and retention without introducing regression.