Machine Learning Engineer, GenRecs, Personalization
Spotify
Machine Learning Engineer, GenRecs, Personalization
Job Description
The Personalization (PZN) team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music, podcasts and audiobooks better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people all over the world use the products we build which include destinations like Home and Search as well as original playlists such as Made For You, Discover Weekly and Daily Mix.
Locations
- New York, New York, United States
Salary
Salary details available upon request
Estimated Salary Rangemedium confidence
220,000 - 400,000 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 learningintermediate
- natural language processingintermediate
- generative AIintermediate
- Pythonintermediate
- Javaintermediate
- Scalaintermediate
- Pytorchintermediate
- TensorFlowintermediate
- Apache Beamintermediate
- Apache Sparkintermediate
- GCPintermediate
- AWSintermediate
Required Qualifications
- An experienced ML practitioner motivated to work on complex real-world problems in a fast-paced and collaborative environment (experience)
- Strong background in machine learning, natural language processing, and generative AI, with experience in applying theory to develop real-world applications (experience)
- Hands-on expertise with implementing end-to-end production ML systems at scale in Python, Java or Scala (experience)
- Experience with designing end-to-end tech specs and modular architectures for ML frameworks in complex problem spaces in collaboration with product teams (experience)
- Experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, and cloud platforms like GCP or AWS (experience)
Preferred Qualifications
- Experience with Pytorch and/or TensorFlow is a strong plus (experience)
Responsibilities
- Design, build, evaluate, and ship ML solutions in Spotify’s personalization products
- Collaborate with cross functional teams spanning user research, design, data science, product management, and engineering to build new product features that advance our mission to connect artists and fans in personalized and useful ways
- Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users
- Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization
- Be part of an active group of machine learning practitioners
Benefits
- general: We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location
- general: This team operates within the Eastern Standard time zone for collaboration
Tags & Categories
Machine Learning Engineer, GenRecs, Personalization
Spotify
Machine Learning Engineer, GenRecs, Personalization
Job Description
The Personalization (PZN) team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music, podcasts and audiobooks better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people all over the world use the products we build which include destinations like Home and Search as well as original playlists such as Made For You, Discover Weekly and Daily Mix.
Locations
- New York, New York, United States
Salary
Salary details available upon request
Estimated Salary Rangemedium confidence
220,000 - 400,000 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 learningintermediate
- natural language processingintermediate
- generative AIintermediate
- Pythonintermediate
- Javaintermediate
- Scalaintermediate
- Pytorchintermediate
- TensorFlowintermediate
- Apache Beamintermediate
- Apache Sparkintermediate
- GCPintermediate
- AWSintermediate
Required Qualifications
- An experienced ML practitioner motivated to work on complex real-world problems in a fast-paced and collaborative environment (experience)
- Strong background in machine learning, natural language processing, and generative AI, with experience in applying theory to develop real-world applications (experience)
- Hands-on expertise with implementing end-to-end production ML systems at scale in Python, Java or Scala (experience)
- Experience with designing end-to-end tech specs and modular architectures for ML frameworks in complex problem spaces in collaboration with product teams (experience)
- Experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, and cloud platforms like GCP or AWS (experience)
Preferred Qualifications
- Experience with Pytorch and/or TensorFlow is a strong plus (experience)
Responsibilities
- Design, build, evaluate, and ship ML solutions in Spotify’s personalization products
- Collaborate with cross functional teams spanning user research, design, data science, product management, and engineering to build new product features that advance our mission to connect artists and fans in personalized and useful ways
- Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users
- Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization
- Be part of an active group of machine learning practitioners
Benefits
- general: We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location
- general: This team operates within the Eastern Standard time zone for collaboration
Tags & Categories