Senior ML Engineer - Real-Time Ad Bidding & LTV

Stealth Talent Solutions; New York (NY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Stealth Talent Solutions in New York City is seeking an experienced machine learning specialist to build and optimize real-time bidding models that predict ad value across CTV and mobile.

You'll design systems that combine multiple estimators, tackle challenges like extreme class imbalance and delayed attribution, and drive production-ready research from hypothesis to A/B testing, partnering with global teams.

Qualifications

• 7+ years in machine learning.
• 5+ years of Python.
• 5+ years building predictive models.
• 3+ years of SQL and statistical analysis.
• Strong experiment design and A/B testing skills.
• MS or PhD in ML, Statistics, Applied Math, or Computer Science preferred.

Responsibilities

• Build and improve conversion and lifetime value (LTV) prediction models for real-time bidding.
• Design systems that combine several estimators to set bid values in real time.
• Solve hard modeling problems: extreme class imbalance, delayed attribution, noisy or incomplete labels, and cold-start users.
• Build targeting and identity features from IP-based signals on CTV inventory.
• Own the research cycle from hypothesis to production, including offline experiments and A/B tests.
• Work with engineering, data infrastructure, and operations teams to ship models to production.
• Help set technical direction alongside applied scientists and engineers in Europe.

Skills

Machine learning

Python

Predictive modeling

SQL

A/B testing

Experiment design

Education

MS or PhD in ML/Statistics/Applied Math/CS

Job description

Stealth Talent Solutions in New York City is seeking an experienced machine learning specialist to build and optimize real-time bidding models that predict ad value across CTV and mobile.

You'll design systems that combine multiple estimators, tackle challenges like extreme class imbalance and delayed attribution, and drive production-ready research from hypothesis to A/B testing, partnering with global teams.