Opportunity at
PinterestDirector, Machine Learning Engineering, Ads Quality
Pinterest operates a visual discovery platform where hundreds of millions of users go to find ideas and decide on activities, purchases, and inspiration. The Ads Quality…
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About This Role
Pinterest operates a visual discovery platform where hundreds of millions of users go to find ideas and decide on activities, purchases, and inspiration. The Ads Quality division builds machine-learning architectures that deliver relevant commercial content to users while driving measurable results for advertisers. The organization is seeking a Director of Machine Learning Engineering, Ads Quality to direct a diverse group of modeling teams focused on engagement, conversion, return on ad…
Job Description
Pinterest operates a visual discovery platform where hundreds of millions of users go to find ideas and decide on activities, purchases, and inspiration. The Ads Quality division builds machine-learning architectures that deliver relevant commercial content to users while driving measurable results for advertisers. The organization is seeking a Director of Machine Learning Engineering, Ads Quality to direct a diverse group of modeling teams focused on engagement, conversion, return on ad spend optimization, ranking, representation learning, and machine learning experimentation.
In this position, the leader will establish the technical vision and multi-year strategy for advertising machine learning, aligning model improvements with marketplace health, revenue, and performance metrics. The role involves guiding engineering managers, senior technical staff, and machine-learning engineers across multiple modeling domains. The ideal candidate will direct the evolution of advertising models toward advanced architectures, including foundation models, multimodal representations, and long-context sequence modeling. Furthermore, the role requires partnering closely with cross-functional partners across product, data science, infrastructure, and measurement to maintain high engineering standards and production reliability.
This opportunity is suited for an experienced machine-learning leader with a history of scaling teams and managing complex recommendation or advertising systems. The role requires working from either the San Francisco or Palo Alto office on a hybrid schedule, necessitating in-person collaboration one to two times per week.
Responsibilities
- Formulate the multi-year technical roadmap and vision for Ads Quality machine learning.
- Manage and mentor a group of engineering managers, senior technical staff, and machine-learning engineers.
- Define a unified modeling strategy covering engagement, conversion, relevance, ranking, and foundation models.
- Improve model calibration, generalization, robustness, attribution, and cold-start performance across Pinterest surfaces.
- Direct the transition toward larger architectures like foundation models, distillation, multimodal representations, and cross-domain learning.
- Coordinate offline evaluation, online experimentation, and production monitoring for modeling investments.
- Collaborate with internal product, data science, signals, retrieval, delivery, measurement, and infrastructure groups.
- Establish standards for training-serving parity, data privacy, latency, reliability, and cost efficiency.
- Enhance engineering velocity through automation, agentic development tools, and reusable workflows.
- Represent Ads Quality machine learning in leadership forums to communicate strategies, risks, and results.
Requirements
- Minimum 12 years of experience in building and deploying machine-learning systems
- Significant background leading managers and multi-team organizations
- Demonstrated success guiding large-scale recommendation, ranking, advertising, search, marketplace, or personalization machine-learning teams
- Thorough understanding of modern deep-learning and recommender-system techniques
- Ability to connect modeling objectives and offline metrics to online experiments and business outcomes
- Experience operating production machine-learning systems with strict requirements for latency, availability, and cost
- Must be located within commutable distance to the San Francisco or Palo Alto offices
- Ability to work in the office 1 to 2 times per week
Qualifications
- Experience with conversion, value, return on ad spend, bidding, or other lower-funnel optimization problems
- Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent experience
- Advanced degree in a related field
Core Skills
Benefits
- Base salary range between $314,580 and $550,515 USD for US-based applicants
- Eligible for equity
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Where is this position located?
This role is based in San Francisco or Palo Alto and requires working in the office for in-person collaboration 1 to 2 times per week.
What is the salary range for this role?
The base salary range for US-based applicants is $314,580 to $550,515 USD, and the position is also eligible for equity.
Is relocation assistance provided?
No, this position is not eligible for relocation assistance.
What kind of experience is required?
Candidates must have a minimum of 12 years of experience building and deploying machine-learning systems, including significant experience leading managers and multi-team organizations.
Sample Interview Questions
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