ML Engineering Manager — Core Ads Ranking & Foundations

Bazeta; United States; Remote; USD 230,000 - 322,000; Full time

Benefits offered by this job

Healthcare Benefits

401k with Employer Match

Global Benefit programs

Family Planning Support

Gender-Affirming Care

Mental Health & Coaching Benefits

Flexible Vacation & Paid VolunteerTime

Paid Parental Leave

Job summary

Reddit is seeking a Machine Learning Engineering Manager for the Core Ads Ranking team in the United States. Lead the vision for foundational models, user understanding, and ML systems, partnering with Retrieval, Shopping, and Prediction teams to scale impact.

You will mentor a high-performing ML group, drive end-to-end model lifecycle from ideation to deployment, and balance long-term investments with milestone-driven progress.

Qualifications

• 1+ years of management experience leading ML-focused teams.
• 6+ years building and scaling machine learning models.
• 4+ years hands-on with TensorFlow or PyTorch.
• End-to-end ML lifecycle experience: training, evaluating, deploying models.
• Experience with foundational models and user/content understanding.
• Ads experience is a plus.

Responsibilities

• Set Technical Vision and Strategy: define and execute a roadmap for foundational models and user understanding.
• Collaborate Cross-Functionally: align with customer teams and infrastructure groups on priorities.
• Drive Technical Execution: oversee model development from ideation to deployment with high ML standards.
• Lead and Mentor a High-Performing Team: recruit, mentor, and retain ML talent.

Skills

Team management

ML leadership

Cross-functional collaboration

ML lifecycle understanding

Strategic vision

Tools

TensorFlow

PyTorch

Job description

Reddit is seeking a Machine Learning Engineering Manager for the Core Ads Ranking team in the United States. Lead the vision for foundational models, user understanding, and ML systems, partnering with Retrieval, Shopping, and Prediction teams to scale impact.

You will mentor a high-performing ML group, drive end-to-end model lifecycle from ideation to deployment, and balance long-term investments with milestone-driven progress.