Senior Machine Learning Engineer - Ad Platforms, Predictions/Personalization
Job Description
Summary
At Apple, we work every single day to build products that enrich people’s lives. Our Advertising Platforms group makes it possible for people around the world to easily access informative and visionary content on their devices while helping publishers and developers promote and monetize their work. Our technology and services power advertising in Apple News and Search Ads in App Store. Our platforms are highly-performant, deployed at scale, and setting new standards for enabling effective advertising while protecting user privacy.
We are looking for an ambitious individual who can thrive in an Agile environment. You will develop and apply advanced techniques and algorithms to improve an ad network. You’ll be developing and implementing new optimization and advertising algorithms. The position also involves developing a keen understanding of the ad network behavior; detecting meaningful data patterns useful for user engagement modeling; assuring the integrity and breadth of the data; measuring user, campaign and app performance; and finally analyzing the results of extremely large-scale experiments. In addition, the successful candidate will also apply advanced ML techniques for designing privacy mechanisms, encryption techniques, pricing/auction strategies and ranking algorithms, by utilizing data available only on the user’s device.
We are looking for an ambitious individual who can thrive in an Agile environment. You will develop and apply advanced techniques and algorithms to improve an ad network. You’ll be developing and implementing new optimization and advertising algorithms. The position also involves developing a keen understanding of the ad network behavior; detecting meaningful data patterns useful for user engagement modeling; assuring the integrity and breadth of the data; measuring user, campaign and app performance; and finally analyzing the results of extremely large-scale experiments. In addition, the successful candidate will also apply advanced ML techniques for designing privacy mechanisms, encryption techniques, pricing/auction strategies and ranking algorithms, by utilizing data available only on the user’s device.
Description
You will have the opportunity to work on Machine Learning models for an Advertising Platform with extreme scale requirements. You should have experience designing, developing and implementing algorithms combining expertise in machine learning, statistics, optimization, personalization or information retrieval and in analyzing impact on marketplace dynamics, preferably within an ad network. You have an excellent understanding of scalable architectures including operational concerns. Your ability to be a great teammate under tight deadline constraints is key to success.
Minimum Qualifications
- MS/PhD in computer science or equivalent field with 4+ years relevant experience
- You will apply advanced statistical / ML methods and work with large, complex data sets.
- You have experience with Systems Software implementation of new algorithms and data engineering / analysis aspects of an ad network in Python and/or Scala and/or Java, or you have research experience in at least three of the following research areas - Algorithms, Artificial Intelligence, Data Mining, Distributed Systems, Machine Learning, Statistics, Personalization, Recommender Systems, Information Retrieval, Natural Language Processing
- You will propose, design and analyze new algorithms to benefit the ad network.
- Proficiency with databases, SQL, Spark and scripting languages.
- Practical understanding of the mathematics behind modern machine learning, linear algebra and statistics.
- Proven ability to apply statistical techniques to tackle real problems.
- Ability to take requirements from design through to implementation both independently and with larger teams.
- Closely working with operational teams on deployment, monitoring, management concerns
Preferred Qualifications
- Familiarity with AI models such as LLMs and ML techniques like such as explore/exploit, that can prove useful in cold-starting engagement (tap, conversion-rate) models