Yahoo DSP Research team develops mathematical models and algorithms for real-time programmatic ad spend optimization. The Principal Research Scientist will lead design and deployment of large-scale budget pacing, bid optimization, and marketplace balancing algorithms. Responsibilities include architecting agentic AI workflows, applying control theory and machine learning to marketplace dynamics, directing AI-assisted code refactoring and testing, owning research initiatives from formulation to production, partnering with engineering and product teams, mentoring senior staff, and communicating strategy to leadership. Requires Ph.D. in quantitative field, 8+ years experience in large-scale optimization or ML production, expertise in Python, Java, C++, SQL, Spark, and AI developer tools like GitHub Copilot and Cursor. Benefits include hybrid work options, healthcare, 401k, childcare backup, education stipends, and bonuses. Salary range $160,965 to $349,885 annually.
What you'll do
Lead research, architectural design, and production deployment of large-scale algorithms for budget pacing, bid optimization, and marketplace supply/demand balancing
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Architect and implement agentic AI workflows using AI coding agents, automated experiment orchestration, and agent-driven validation
Provide technical strategy and long-term vision for DSP optimization ecosystem aligned with business and engineering leadership
Apply control theory, convex/stochastic optimization, and machine learning to solve real-time marketplace dynamics under signal uncertainty
Direct AI-assisted code refactoring, automated testing suite generation, and LLM-driven research synthesis using modern AI tools
Own multi-quarter research initiatives from mathematical formulation through production rollout with rigorous metrics and evaluation
Partner with engineering and product management teams to ensure scalable algorithmic execution
Mentor senior scientists and engineers, fostering AI-forward research culture and technical excellence
Communicate technical strategy, experimental results, and architectural decisions to executives and cross-functional partners
Requirements
Ph.D. degree in Computer Science, Electrical Engineering, Operations Research, Statistics, Mathematics, or related quantitative field
8+ years of experience designing and deploying large-scale optimization, control systems, or machine learning models in production
Strong foundation in probability, convex/stochastic optimization, and control theory
Expertise in production programming languages (Python, Java, C++, SQL, Spark) with experience in low-latency or distributed data architectures
Hands-on experience with AI pair-programming and developer automation tools (GitHub Copilot, Cursor, Claude Code, Codex, agentic orchestration frameworks)
Ability to evaluate, refine, and validate AI-generated research artifacts and code ensuring safety, accuracy, and performance
Mindset of continuous experimentation leveraging AI tools to automate engineering and diagnostic tasks
Strong communication skills to translate complex mathematical concepts into executive strategy
Flexible hybrid work optionsHealthcare401k planBackup childcareEducation stipendsDiscretionary annual bonus or commissionsInclusive and diverse workplace with employee resource groups
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