Sr. Principal Machine Learning EngineerComcast • San Francisco, California, United States
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Sr. Principal Machine Learning Engineer
Comcast
- San Francisco, California, United States
- San Francisco, California, United States
À propos
Key Responsibilities
Define and execute the long‑term research strategy for optimization, targeting, bidding, forecasting, and marketplace systems.
Lead a global team of research scientists and applied researchers across multiple geographies.
Identify emerging opportunities in machine learning, artificial intelligence, optimization, and computational economics.
Foster a culture of scientific rigor, innovation, experimentation, and measurable business impact.
Machine Learning & Optimization
Drive development of large‑scale machine learning systems for prediction, recommendation, targeting, and campaign optimization.
Advance state‑of‑the‑art approaches in:
Predictive modeling
Reinforcement learning
Causal inference
Auction theory
Multi‑objective optimization
Control systems
Operations research
Oversee research from problem formulation through experimentation, deployment, and production monitoring.
Marketplace & Platform Strategy
Lead innovation across demand‑side platform (DSP) optimization, campaign delivery, supply optimization, and audience targeting systems.
Partner with Product and Engineering leaders to align research investments with strategic business objectives.
Develop frameworks that improve advertiser outcomes, platform efficiency, and marketplace health.
Organizational Leadership
Recruit, mentor, and retain top‑tier scientific talent.
Establish technical standards, research processes, and career development frameworks.
Build collaborations across research, engineering, product, and commercial organizations.
Represent the company externally through publications, patents, industry conferences, and academic partnerships.
Business Impact
Translate advanced research into measurable improvements in revenue, efficiency, customer performance, and platform scalability.
Drive data‑driven decision‑making through experimentation and causal measurement frameworks.
Evaluate strategic technology investments and emerging opportunities.
Required Qualifications
Ph.D. in Electrical Engineering, Computer Science, Operations Research, Applied Mathematics, Statistics, Economics, or related quantitative field.
15+ years of experience developing machine learning and optimization systems.
10+ years of leadership experience managing research scientists and technical organizations.
Demonstrated success deploying research innovations into production environments at scale.
Deep expertise in several of the following:
Machine Learning
Optimization
Operations Research
Control Theory
Economics and Auction Design
Statistical Modeling
Large‑Scale Experimentation
Track record of publications, patents, or significant scientific contributions.
Exceptional communication and executive stakeholder management skills.
Experience leading research organizations within digital advertising, marketing technology, marketplaces, or internet‑scale platforms.
Expertise in demand‑side platforms (DSPs), bidding systems, targeting technologies, or advertising optimization.
Experience managing globally distributed teams.
Strong record of academic and industry thought leadership.
Success Metrics
Measurable improvements in platform performance and optimization outcomes.
Successful deployment of research innovations into production systems.
Growth and retention of scientific talent.
Patent generation and publication output.
Revenue impact attributable to optimization and machine learning initiatives.
Strategic advancement of the company’s AI and optimization capabilities.
Ideal Candidate Profile A recognized leader in applied machine learning and optimization research who can bridge cutting‑edge science and business outcomes. This executive is equally comfortable discussing auction theory, reinforcement learning, organizational strategy, and executive‑level business priorities, while building teams capable of solving some of the industry's most challenging large‑scale optimization problems.
Salary Primary Location Pay Range: This job can be performed in California with a good faith estimated pay range upon hire of $288,594.24 - $384,792.31 USD.
Comcast intends to offer the selected candidate base pay within the posted range for this role at the time of posting dependent on job‑related, non‑discriminatory factors such as experience. The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later.
For more details, please visit our compensation and benefits summary on our careers site.
Equal Opportunity Employer Comcast is an equal opportunity workplace. We will consider all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, genetic information, or any other basis protected by applicable law. Comcast will consider for employment applicants with arrest or conviction records in accordance with the requirements of applicable law, including the San Francisco Fair Chance Ordinance, the Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Please note that federal state, or local laws and regulations may restrict or prohibit Comcast from hiring individuals convicted of certain crimes. Additionally, an applicant’s criminal history may have a direct, adverse, and negative relationship on the job duties of this position, which may result in the withdrawal of a conditional offer of employment.
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Compétences linguistiques
- English
Avis aux utilisateurs
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