below marketmost similar roles pay hereabove market
This role pays more than
96%
of similar roles. Most pay
$214,000–$285,625
— the blue band above.
At the midpoint, this role pays about
$433k
versus about
$250k
for comparable roles.
Based on 240 similar postings.
Employer
About Pinterest
Pinterest is a visual discovery and inspiration platform where people find ideas for home, style, recipes, and more. It serves hundreds of millions of users worldwide through its image and video pinboard product.
Pinterest currently has
69 open roles
on FindRole.
Listed pay typically runs
$175,631–$339,078
across 62 roles with salary data.
The Director, Machine Learning Engineering, Ads Quality leads a broad portfolio of modeling teams within the Ads Quality organization. This leader establishes the technical vision and multi-year strategy for machine learning systems that ensure ads are relevant and valuable for users while delivering meaningful outcomes for advertisers. Key responsibilities include managing engineering managers and senior technical leaders to drive improvements in engagement ranking, oCPM, conversion modeling, ROAS optimization, and retrieval models. The role requires expertise in deep learning, recommender systems, sequence models, embeddings, multimodal models, multi-task learning, foundation models, distillation, and reinforcement learning. The position focuses on solving complex marketplace problems by balancing model quality, calibration, and generalization with production requirements like latency, privacy, reliability, and cost efficiency across various platform surfaces.
Set the multi-year technical vision and strategy for Ads Quality machine learning models.
Lead and develop a group of engineering managers and senior machine-learning engineers.
Establish a coherent modeling roadmap for engagement, conversion, ROAS, and foundation-model initiatives.
Drive improvements in model quality, calibration, generalization, and robustness across Pinterest surfaces.
Guide the evolution of Ads models toward foundation models, distillation, and multimodal representations.
Ensure modeling investments translate into reliable production outcomes through offline evaluation and online experimentation.
Improve engineering velocity through reusable modeling infrastructure, automation, and agentic development tools.
Represent Ads Quality ML in senior leadership forums to communicate strategy, risks, and results.
What we're looking for
Minimum 12 years of experience building and deploying machine-learning systems.
Significant experience leading managers and multi-team organizations.
Demonstrated success leading large-scale recommendation, ranking, advertising, search, marketplace, or personalization ML teams.
Strong understanding of modern deep-learning and recommender-system techniques, including sequence models, embeddings, multimodal models, and foundation models.
Experience with conversion, value, ROAS, bidding, or other lower-funnel optimization problems (preferred).
Experience operating production ML systems with demanding requirements for latency, availability, calibration, privacy, reliability, and cost.
Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent experience.
Machine Learning
Data Platforms
Large-scale Data Processing
Streaming Systems
Experimentation
Anomaly Detection
Graph Systems
Privacy-Enhancing Technologies
Model Governance
Production Reliability
Causal Measurement
AI
Agentic Systems
data-quality
Machine Learning
Deep Learning
LLMs
Natural Language Processing
Recommender Systems
Hadoop
Spark
SQL
Data Processing Pipelines
Reinforcement Learning
Graph Representation Learning
realtime systems
Computational Advertising