Lead Research Scientist — AI-Driven DSP Optimization
Yahoo
Get a reply from this employer — a resume and cover letter tailored to exactly what they’re hiring for.
Benefits offered by this job
Job summary
Yahoo is seeking a Principal Research Scientist (IC6) to lead the next-generation budget control, pacing, and bidding algorithms within its DSP Optimization team. You will drive AI-augmented development loops, mentor senior staff, and align research with business goals to deliver scalable, safe, and high-performance solutions.
This role emphasizes production-ready research, cross-functional leadership, and a strong mathematical foundation in optimization and control theory to optimize real-time
Qualifications
- PhD in CS/EE/OR/Stats/Math or related quantitative field.
- 8+ years designing and deploying large-scale optimization, control systems, or ML models in production.
- Strong foundation in probability, convex/stochastic optimization, and control theory.
- Experience with production languages: Python, Java, C++, SQL, or Spark.
- Hands-on experience with AI pair-programming and developer automation tools.
Responsibilities
- Lead the research, architectural design, and production deployment of large-scale optimization algorithms.
- Architect and drive agentic AI workflows and automated experiment orchestration.
- Define technical strategy and long-term vision for the DSP optimization ecosystem.
- Apply control theory and advanced optimization to real-time marketplace dynamics.
- Mentor senior scientists and engineers; set technical excellence standards.
Skills
Education
Tools
Job description
Yahoo is seeking a Principal Research Scientist (IC6) to lead the next-generation budget control, pacing, and bidding algorithms within its DSP Optimization team. You will drive AI-augmented development loops, mentor senior staff, and align research with business goals to deliver scalable, safe, and high-performance solutions.
This role emphasizes production-ready research, cross-functional leadership, and a strong mathematical foundation in optimization and control theory to optimize real-time
'/%3e%3cpath%20d='M101.744%2012.4726L102.894%2016.1947L99.0021%2013.2201L101.744%2012.4726ZM105.3%208.40937H100.488L99.0026%203.59473L97.5116%208.41047L92.7%208.40385L96.5965%2011.3834L95.1055%2016.1942L99.0021%2013.2196L101.408%2011.3834L105.3%208.40992V8.40937Z'%20fill='white'/%3e%3cdefs%3e%3clinearGradient%20id='paint0_linear_5593_1381'%20x1='90'%20y1='9.44471'%20x2='99'%20y2='9.44471'%20gradientUnits='userSpaceOnUse'%3e%3cstop%20stop-color='%2300AD70'/%3e%3cstop%20offset='0.998594'%20stop-color='%2300B67A'/%3e%3cstop%20offset='1'%20stop-color='%23DCDCE6'/%3e%3c/linearGradient%3e%3c/defs%3e%3c/svg%3e){kind=small}
