Machine Learning Engineer, Monetization Engineering
Machine Learning Engineer, Monetization Engineering
Job Description
Responsibilities
- Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
- Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search)
- Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
- Work in a high-impact environment with quick experimentation and product launches
- Keep up with industry trends in recommendation systems
- Leverage LLMs to enhance content understanding
Qualifications
- 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)
- Degree in computer science, machine learning, statistics, or related field
- End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
- Practical knowledge of large scale recommender systems, or modern ads ranking, retrieval, targeting, marketplace systems
Preferred Qualifications
- Publications at top ML conferences
- Expertise in scalable realtime systems that process stream data
- Passion for applied ML and the Pinterest product
- Background in computational advertising
Locations
- San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US
Salary
163,418 - 285,982 USD / yearly
Skills Required
- Machine learning methods including user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, graph representation learningintermediate
- Building data processing pipelinesintermediate
- Large scale machine learning systemsintermediate
- Big data technologies (Hadoop/Spark)intermediate
- Large scale recommender systemsintermediate
- Modern ads ranking, retrieval, targeting, marketplace systemsintermediate
- Deep learningintermediate
- Data driven methods for candidate retrievalintermediate
- LLMs for content understandingintermediate
Required Qualifications
- 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning) (experience)
- Degree in computer science, machine learning, statistics, or related field (experience)
- End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark) (experience)
- Practical knowledge of large scale recommender systems, or modern ads ranking, retrieval, targeting, marketplace systems (experience)
Preferred Qualifications
- Publications at top ML conferences (experience)
- Expertise in scalable realtime systems that process stream data (experience)
- Passion for applied ML and the Pinterest product (experience)
- Background in computational advertising (experience)
Responsibilities
- Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
- Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search)
- Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
- Work in a high-impact environment with quick experimentation and product launches
- Keep up with industry trends in recommendation systems
- Leverage LLMs to enhance content understanding
Tags & Categories
Machine Learning Engineer, Monetization Engineering
Machine Learning Engineer, Monetization Engineering
Job Description
Responsibilities
- Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
- Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search)
- Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
- Work in a high-impact environment with quick experimentation and product launches
- Keep up with industry trends in recommendation systems
- Leverage LLMs to enhance content understanding
Qualifications
- 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)
- Degree in computer science, machine learning, statistics, or related field
- End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
- Practical knowledge of large scale recommender systems, or modern ads ranking, retrieval, targeting, marketplace systems
Preferred Qualifications
- Publications at top ML conferences
- Expertise in scalable realtime systems that process stream data
- Passion for applied ML and the Pinterest product
- Background in computational advertising
Locations
- San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US
Salary
163,418 - 285,982 USD / yearly
Skills Required
- Machine learning methods including user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, graph representation learningintermediate
- Building data processing pipelinesintermediate
- Large scale machine learning systemsintermediate
- Big data technologies (Hadoop/Spark)intermediate
- Large scale recommender systemsintermediate
- Modern ads ranking, retrieval, targeting, marketplace systemsintermediate
- Deep learningintermediate
- Data driven methods for candidate retrievalintermediate
- LLMs for content understandingintermediate
Required Qualifications
- 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning) (experience)
- Degree in computer science, machine learning, statistics, or related field (experience)
- End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark) (experience)
- Practical knowledge of large scale recommender systems, or modern ads ranking, retrieval, targeting, marketplace systems (experience)
Preferred Qualifications
- Publications at top ML conferences (experience)
- Expertise in scalable realtime systems that process stream data (experience)
- Passion for applied ML and the Pinterest product (experience)
- Background in computational advertising (experience)
Responsibilities
- Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
- Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search)
- Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
- Work in a high-impact environment with quick experimentation and product launches
- Keep up with industry trends in recommendation systems
- Leverage LLMs to enhance content understanding
Tags & Categories