As a technical leader in Ads Products, you will shape the long-term engineering direction of ML-powered advertiser products. You will connect campaign optimization and advertiser-experience opportunities with the models, data, and product systems needed to deliver them, guiding cross-team engineering efforts from technical strategy through measurable advertiser impact.
Responsibilities
Define a multi-year technical roadmap for ML-powered campaign optimization or advertiser experiences, connecting near-term product work to long-term advertiser value.
• Guide model and software architecture across the data, services, and application integrations needed for dependable ML products.
• Drive cross-functional engineering efforts with product, data science, and engineering partners to move capabilities from evaluation to production impact.
• Use data and experimentation to evaluate model and product decisions and communicate technical trade-offs to engineering and executive stakeholders.
• Mentor and influence engineers and technical leaders across teams, improving model evaluation, engineering quality, and reliable product delivery.
Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 12+ years of programming experience in a relevant language OR 8+ years experience with a PhD
• Proven track record of planning multi-year roadmap in which short-term projects ladder to the long-term mission
• Experience driving large cross-functional/industry-wide engineering efforts
• Experience utilizing data and analysis to explain technical problems and provide detailed feedback and solutions
• Experience communicating and working across functions to drive solutions
• Experience mentoring/influencing executive stakeholders across organizations
Preferred Qualifications
• Publications, patents, or open-source contributions that shaped the recommendations or ranking field
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
