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Senior Machine Learning Engineer (Ad Serving)

Roku · Boston, United States

External listingfull-timeabout 2 months ago

About The Role

Join Roku as a Senior Machine Learning Engineer, where you will drive improvements in ad serving efficiency through advanced data analytics and machine learning techniques. You will translate business problems into actionable strategies, develop advanced ML models, design and run live experiments, and build production-grade ML systems. You will also partner with cross-functional stakeholders to define and drive the ML strategy for Ad Serving, and provide technical leadership and mentorship in machine learning and applied research best practices.

  • Drive improvements in Roku Exchange efficiency by applying advanced data analytics and machine learning techniques to optimize pricing, allocation, and auction across the marketplace.
  • Translate ambiguous business problems into clear hypotheses, measurable objectives, and actionable strategies, and develop advanced ML models to drive product innovation.
  • Design and run live experiments to prototype improvements and validate their effectiveness, and build production-grade ML systems that operate reliably in large-scale, low-latency ad serving environments.
  • Deep expertise in statistics, machine learning, and optimization techniques
  • Excellent written and verbal communication and collaboration skills
  • 10+ years of industry experience applying machine learning and data science to solve complex, mission-critical real-world problems
  • Proven track record of leading cross-functional teams and initiatives
  • Expertise in advertising is a plus
  • Strong CS fundamentals with the ability to translate ideas into clean, efficient code with ease
  • Solid software engineering skills in languages such as Python, SQL, and Java/Scala
  • Perform as a staff+ technologist on complex, revenue-critical, large-scale distributed ML systems
  • Hands-on experience building and deploying ML models in production, including feature engineering, model serving, monitoring, and continuous improvement
  • Publications in leading peer-reviewed conferences or academic journals are a plus

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