Machine Learning Engineer II, Messaging Optimization

Key Skills

AWS, Data pipelines, Google Cloud Platform (GCP), Machine learning techniques, Python Programming, PyTorch, TensorFlow

Job Description

We're looking for a Machine Learning Engineer II to join our team focusing on messaging optimization. Through our messaging platform, we communicate with users to connect them with valuable audio content and to help the business grow.

The team's vision is to build the machine learning models and infrastructure that offers a fully personalized and ML-optimized experience for listeners throughout their user journey that powers every messaging and conversion campaign at Spotify.

Our team is a combination of Machine Learning Engineers, Data Engineers, Backend Engineers and Data Scientists.

What You'll Do

• Contribute to designing, building, evaluating, shipping, and refining Spotify’s product by hands-on ML development.
• Collaborate with a multi-functional agile team 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 relevant ways.
• Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users.
• Help drive optimisation, testing, and tooling to improve quality.
• Be part of an active group of machine learning practitioners in your mission and across Spotify.

Who You Are

• You have a strong background in machine learning, theory, and practice.
• You are comfortable explaining the intuition and assumptions behind ML concepts, experience in the messaging space is a plus.
• You have hands-on experience implementing and maintaining production ML systems in Python, Scala and using libraries like Tensorflow or PyTorch.
• You are experienced with building data pipelines, and you are self-sufficient in getting the data you need to build and evaluate your models.
• You preferably have experience with cloud platforms like GCP or AWS.

Where You'll Be

• For this role you will be based in London.