Principal Research Scientist at Yahoo

Job description

A little about us The Yahoo DSP Research team sits at the core of our programmatic ad platform, developing the mathematical models and algorithms that govern billions of dollars in real-time ad spend daily. We blend control theory, machine learning, and economics with modern AI-driven engineering practices to build world-class marketplace optimization systems. About the Role As a Principal Research Scientist (IC6) on the Yahoo DSP Optimization team, you will serve as a primary technical authority driving the next-generation architecture for budget control, pacing, and bidding algorithms. In this role, you will lead the evolution of our high-scale optimization stack by integrating agentic AI practices—shifting traditional research-to-production pipelines into highly automated, AI-augmented development loops. Responsibilities Lead the research, architectural design, and production deployment of large-scale algorithms for budget pacing, bid optimization, and marketplace supply/demand balancing. Architect and spearhead the implementation of agentic AI workflows—utilizing AI coding agents, automated experiment orchestration, and agent-driven validation to dramatically accelerate research-to-production iteration velocity. Provide technical strategy and long-term vision for the DSP optimization ecosystem in close alignment with business and engineering leadership. Apply control theory, convex/stochastic optimization, and machine learning to solve complex, real-time marketplace dynamics under signal uncertainty. Direct AI-assisted code refactoring, automated testing suite generation, and LLM-driven research synthesis using modern tools (e.g., Cursor, GitHub Copilot, Gemini, Claude, ChatGPT) to elevate team engineering standards. Own multi-quarter research initiatives from mathematical formulation through production rollout, establishing rigorous metrics and evaluation frameworks. Partner closely with engineering and product management teams to ensure algorithmic designs execute seamlessly at scale. Mentor senior scientists and engineers, setting technical excellence standards and fostering an AI-forward research culture. Communicate technical strategy, experimental results, and architectural choices clearly to executive and cross-functional partners. Basic Qualifications Ph.D. degree in Computer Science, Electrical Engineering, Operations Research, Statistics, Mathematics, or a related quantitative field. 8 years of hands-on experience designing and deploying large-scale optimization, control systems, or machine learning models in production environments. Deep mathematical foundation in probability, convex/stochastic optimization, and control theory (e.g., feedback control, model predictive control). Demonstrated expertise in production programming languages (Python, Java, C , SQL, or Spark) with experience in low-latency or distributed data architectures. Active hands-on experience with AI pair-programming and developer automation tools (e.g., GitHub Copilot, Cursor, Claude Code, Codex, agentic orchestration frameworks). Proven capability to evaluate, refine, and validate AI-generated research artifacts and code, ensuring strict safety, accuracy, and performance standards. Demonstrated mindset of continuous experimentation, leveraging AI tools to automate repetitive engineering and diagnostic tasks. Strong communication skills with a proven track record of translating complex mathematical concepts into clear executive strategy. Preferred Qualifications Prior research or production experience in computational advertising, real-time bidding (RTB) auctions, or dual-sided marketplace design. Portfolio of peer-reviewed publications or patents in computational advertising, control theory, or applied optimization. Experience leading system-level re-architectures utilizing agentic workflows or automated research pipelines. Familiarity with high-throughput, low-latency distributed computing frameworks. Within your first year, you will: Deliver measurable efficiency gains in budget delivery and bid efficiency by deploying refreshed control models built via AI-augmented development loops. Establish standardized agentic development frameworks across the optimization research organization, setting a benchmark for scientific velocity. Serve as the recognized technical lead for DSP optimization, aligning research roadmaps with long-term business growth. The material job duties and responsibilities of this role include those listed above as well as adhering to Yahoo policies ; exercising sound judgment ; working effectively, safely and inclusively with others ; exhibiting trustworthiness and meeting expectations ; and safeguarding business operations and brand integrity. At Yahoo, we offer flexible hybrid work options that our employees love! While most roles don’t require regular office attendance, you may occasionally be asked to attend in-person events or team sessions. You’ll always get notice to make arrangements. Your recruiter will let you know if a specific job requires regular attendance at a Yahoo office or facility. If you have any questions about how this applies to the role, just ask the recruiter! Yahoo is proud to be an equal opportunity workplace. All qualified applicants will receive consideration for employment without regard to, and will not be discriminated against based on age, race, gender, color, religion, national origin, sexual orientation, gender identity, veteran status, disability or any other protected category. Yahoo will consider for employment qualified applicants with criminal histories in a manner consistent with applicable law. Yahoo is dedicated to providing an accessible environment for all candidates during the application process and for employees during their employment. If you need accessibility assistance and/or a reasonable accommodation due to a disability, please submit a request via the Accommodation Request Form ( www.yahooinc.com/careers/contact-us.html ) or call 1.866.772.3182 . Requests and calls received for non-disability related issues, such as following up on an application, will not receive a response. We believe that a diverse and inclusive workplace strengthens Yahoo and deepens our relationships. When you support everyone to be their best selves, they spark discovery, innovation and creativity. Among other efforts, our 11 employee resource groups (ERGs) enhance a culture of belonging with programs, events and fellowship that help educate, support and create a workplace where all feel welcome. The compensation for this position ranges from $160,965.00 - $349,885.00/yr and will vary depending on factors such as your location, skills and experience.The compensation package may also include incentive compensation opportunities in the form of discretionary annual bonus or commissions. Our comprehensive benefits include healthcare, a great 401k, backup childcare, education stipends and much (much) more. Currently work for Yahoo? Please apply on our internal career site.

Requirements

Must-haves

Responsibilities Lead the research, architectural design, and production deployment of large-scale algorithms for budget pacing, bid optimization, and marketplace supply/demand balancing.
Provide technical strategy and long-term vision for the DSP optimization ecosystem in close alignment with business and engineering leadership.
Apply control theory, convex/stochastic optimization, and machine learning to solve complex, real-time marketplace dynamics under signal uncertainty.
Direct AI-assisted code refactoring, automated testing suite generation, and LLM-driven research synthesis using modern tools (e.g., Cursor, GitHub Copilot, Gemini, Claude, ChatGPT) to elevate team engineering standards.
Own multi-quarter research initiatives from mathematical formulation through production rollout, establishing rigorous metrics and evaluation frameworks.
Partner closely with engineering and product management teams to ensure algorithmic designs execute seamlessly at scale.
Mentor senior scientists and engineers, setting technical excellence standards and fostering an AI-forward research culture.
Communicate technical strategy, experimental results, and architectural choices clearly to executive and cross-functional partners.
Basic Qualifications Ph.D. degree in Computer Science, Electrical Engineering, Operations Research, Statistics, Mathematics, or a related quantitative field.
8 years of hands-on experience designing and deploying large-scale optimization, control systems, or machine learning models in production environments.
Deep mathematical foundation in probability, convex/stochastic optimization, and control theory (e.g., feedback control, model predictive control).
Demonstrated expertise in production programming languages (Python, Java, C , SQL, or Spark) with experience in low-latency or distributed data architectures.
Active hands-on experience with AI pair-programming and developer automation tools (e.g., GitHub Copilot, Cursor, Claude Code, Codex, agentic orchestration frameworks).
Proven capability to evaluate, refine, and validate AI-generated research artifacts and code, ensuring strict safety, accuracy, and performance standards.
Demonstrated mindset of continuous experimentation, leveraging AI tools to automate repetitive engineering and diagnostic tasks.
Strong communication skills with a proven track record of translating complex mathematical concepts into clear executive strategy.
Portfolio of peer-reviewed publications or patents in computational advertising, control theory, or applied optimization.
Experience leading system-level re-architectures utilizing agentic workflows or automated research pipelines.
Familiarity with high-throughput, low-latency distributed computing frameworks.
Within your first year, you will: Deliver measurable efficiency gains in budget delivery and bid efficiency by deploying refreshed control models built via AI-augmented development loops.
Establish standardized agentic development frameworks across the optimization research organization, setting a benchmark for scientific velocity.
Serve as the recognized technical lead for DSP optimization, aligning research roadmaps with long-term business growth.
Currently work for Yahoo? Please apply on our internal career site.

Nice-to-haves

Preferred Qualifications Prior research or production experience in computational advertising, real-time bidding (RTB) auctions, or dual-sided marketplace design.

Application prep

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Keywords to mirror

machine learningaillmpythonjavasqlaccessibility

Platform: workday
Effort: long

Benefits

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Vision

401k