Senior Machine Learning Engineer, Ads Modeling

DataJobs; Mountain View (CA); On-site; USD 149,000 - 245,000; Full time

Benefits offered by this job

Health insurance

Employee stock ownership

Commute subsidy

Vacation and personal days

Parental leave support

Office snacks

Wellbeing programs

ERGs

Job summary

Unity Technologies SF is seeking a Senior Machine Learning Engineer to advance advertising recommendations and personalized ad experiences. You will work on foundational model research, ads understanding, and rapid validation at scale to push business outcomes.

The role requires hands-on ML with PyTorch and Python, strong data analysis skills, and collaboration with engineering, product, and data teams. Location is Mountain View, onsite, with competitive compensation.

Qualifications

• Hands-on experience applying state-of-the-art machine learning and deep learning approaches to complex real-world problems, specifically in user behavior modeling.
• Proficiency in Python and PyTorch, and deep learning frameworks.
• Strong understanding of metric design, large-scale data analysis, and distributed computing frameworks.
• Proven ability to drive projects end-to-end, address cross-team problems, and deliver measurable business outcomes.

Responsibilities

• Build a strong understanding of the ads business, including the product offering, advertiser goals, the ads serving funnel, and rich real-time and dynamic user interaction data.
• Lead foundational research for new machine learning models and paradigms.
• Use rich user data and large-scale compute to validate models quickly and iteratively.
• Partner closely with engineering and product teams to turn research into production-ready systems.

Skills

User behavior modeling

Python

Deep learning

Metric design

Large-scale data analysis

Distributed computing

Tools

PyTorch

Job description

Unity Technologies SF is hiring a Senior Machine Learning Engineer to support Unity Vector’s Ads Modeling team in advancing advertising recommendation algorithms. The role is centered on building systems that can deliver more personalized ads experiences, with a focus on ads business understanding, foundational model research, rapid validation using large-scale compute, and productionizing research alongside engineering and product teams.

What you’ll do

• Build a strong understanding of the ads business, including the product offering, advertiser goals, the ads serving funnel, and rich real-time and dynamic user interaction data.
• Lead foundational research for new machine learning models and paradigms.
• Use rich user data and large-scale compute to validate models quickly and iteratively.
• Partner closely with engineering and product teams to turn research into production-ready systems.

What you bring

• Hands-on experience applying state-of-the‑art machine learning and deep learning approaches to complex real‑world problems, specifically in user behavior modeling.
• Proficiency in Python, PyTorch, and deep learning frameworks.
• Strong understanding of metric design, large-scale data analysis, and distributed computing frameworks.
• Proven ability to drive projects end‑to‑end, address critical cross‑team problems, and deliver measurable business outcomes.

Tech stack

• Python
• PyTorch

Location and salary

The role is based in Mountain View, CA (onsite). The anticipated base salary range is:

• Zone A: USD 188,200 - 244,600 (yearly)
• Zone B: USD 167,200 - 217,300 (yearly)
• Zone C: USD 148,700 - 193,300 (yearly)

In addition to base salary, the position may be eligible for equity awards and participation in company incentive plans (such as annual discretionary bonuses or sales commissions). The final offer will depend on factors including geographic location and the candidate’s relevant experience, professional background, and skill set.

Benefits

• Comprehensive health, life, and disability insurance
• Commute subsidy
• Employee stock ownership
• Competitive retirement/pension plans
• Generous vacation and personal days
• Support for new parents through leave and family‑care programs
• Office food snacks
• Mental Health and Wellbeing programs and support
• Employee Resource Groups
• Global Employee Assistance Program
• Training and development programs
• Volunteering and donation matching program