Machine Learning Engineer II
Machine Learning Engineer II
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), while gaining knowledge of how ML works in different areas
- 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
- Keeping up with industry trends in recommendation systems
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)
- End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
- M.S. or PhD in Machine Learning or related areas
- Expertise in scalable realtime systems that process stream data
- Passion for applied ML and the Pinterest product
Locations
- Toronto, ON, CA
Salary
Estimated Salary Rangemedium confidence
180,000 - 250,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine learning methods including user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learningintermediate
- Building data processing pipelinesintermediate
- Large scale machine learning systemsintermediate
- Big data technologies (e.g., Hadoop/Spark)intermediate
- Scalable realtime systems that process stream dataintermediate
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)
- 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)
- M.S. or PhD in Machine Learning or related areas (experience)
- Expertise in scalable realtime systems that process stream data (experience)
- Passion for applied ML and the Pinterest product (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), while gaining knowledge of how ML works in different areas
- 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
- Keeping up with industry trends in recommendation systems
Tags & Categories
Machine Learning Engineer II
Machine Learning Engineer II
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), while gaining knowledge of how ML works in different areas
- 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
- Keeping up with industry trends in recommendation systems
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)
- End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
- M.S. or PhD in Machine Learning or related areas
- Expertise in scalable realtime systems that process stream data
- Passion for applied ML and the Pinterest product
Locations
- Toronto, ON, CA
Salary
Estimated Salary Rangemedium confidence
180,000 - 250,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine learning methods including user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learningintermediate
- Building data processing pipelinesintermediate
- Large scale machine learning systemsintermediate
- Big data technologies (e.g., Hadoop/Spark)intermediate
- Scalable realtime systems that process stream dataintermediate
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)
- 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)
- M.S. or PhD in Machine Learning or related areas (experience)
- Expertise in scalable realtime systems that process stream data (experience)
- Passion for applied ML and the Pinterest product (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), while gaining knowledge of how ML works in different areas
- 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
- Keeping up with industry trends in recommendation systems
Tags & Categories