The Ad Platform research team at Disney Entertainment and ESPN Product & Technology is seeking a lead machine learning engineer to transform advertising and Disney's Ad platform with AI and data science. The goal is to build solutions to measure and optimize every aspect of the advertising lifecycle, focusing on prediction or optimization engines for addressable ad platforms.
Requirements
- At least 7 years of working experience on large scale machine learning at leading internet companies.
- Strong knowledge of AI/ML technologies, mathematics and statistics.
- Proficient in Java, Python, Scala, large scale ML/DL platforms and processing tech stack.
- Experience with forecasting algorithm is preferred.
- Experience in digital video advertising or digital marketing domain
- Experience with CTR/CVR model, generative AI, forecasting model.
- Experience with Tensorflow, Kubeflow, Databricks or Sagemaker
Responsibilities
- Drive ground-breaking innovation and apply state of the art AI and machine learning in a variety of areas to enhance every aspect of advertising, including inventory forecasting, ad experience, ad pacing, pricing, targeting, and efficient ad delivery.
- Invent and iterate novel solutions for complex ad challenges with fast turnaround.
- Lead the ad algorithm architecture design and iteration of the advertising system.
- Develop scalable and efficient approaches for large scale data analysis and model development.
- Build and experiment new algorithms and models end-to-end, from production rollout to continuous optimization.
- Collaborate with engineering teams, product managers, and program managers in an open and creative environment.
- Mentor team members and support their technical growth.
Other
- Bachelors degree (MS or PhD preferred) in computer science or equivalent experience
- Passionate about understanding the ad business, applying advanced research to relevant business scenarios, and seeking innovation opportunities to enhance business effectiveness.
- Passionate about technology, open to interdisciplinary collaborations, and experienced in building data-driven services and applications.
- A proven track record of thriving in a fast-paced, data-driven, and collaborative work environment is required.
