Principal ML Engineer, Ads Modeling – Production

DataJobs; New York (NY); On-site; USD 223,000 - 325,000; Full time

Benefits offered by this job

Commuting subsidy

Employee stock ownership

Generous vacation and personal days

Training and development programs

Job summary

Unity Technologies SF is hiring a Principal Machine Learning Engineer for Ads Modeling to advance ads recommendation algorithms and personalized advertising experiences. Based in New York, NY (onsite), you will model user behavior and productionize research in collaboration with engineering and product teams.

You will work on a Vector Ads Modeling program to transform research into scalable solutions, balancing rigorous experimentation with real-world impact and cross-team collaboration across a

Qualifications

• Hands-on experience applying state-of-the-art ML and deep learning to real-world problems, focusing on user behavior modeling.
• Proficiency in Python and PyTorch, including deep learning frameworks.
• Strong understanding of metric design, large-scale data analysis, and distributed computing.
• Proven ability to drive projects end-to-end and deliver measurable business results.

Responsibilities

• Identify opportunities and define a modeling roadmap aligned with customer needs.
• Lead foundational research to explore new models and paradigms.
• Use rich user data and large-scale compute to validate models rapidly.
• Collaborate with engineering and product teams to productionalize research.

Skills

ML modeling

Python

PyTorch

Data analysis

Distributed computing

Project leadership

Tools

Python

PyTorch

Job description

Unity Technologies SF is hiring a Principal Machine Learning Engineer for Ads Modeling to advance ads recommendation algorithms and personalized advertising experiences. Based in New York, NY (onsite), you will model user behavior and productionize research in collaboration with engineering and product teams.

You will work on a Vector Ads Modeling program to transform research into scalable solutions, balancing rigorous experimentation with real-world impact and cross-team collaboration across a