Senior Machine Learning Engineer
@ Spotify

Toronto, ON
CA$150,000
On Site
Full Time
Posted 1 day ago

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Job Details

About the Role

The Senior Machine Learning Engineer position on the Hendrix ML Platform team at Spotify is focused on developing a robust, Spotify-wide AI/ML platform for training and serving machine learning models. This platform streamlines the productionization of models and simplifies backend service creation for serving predictions.

What You'll Do

  • Contribute to the Spotify ML Platform SDK and build tools for ML operations.
  • Collaborate with ML Engineers, researchers, and product teams.
  • Work independently and in squads to learn and apply new technologies.
  • Manage large-scale production Kubernetes clusters for ML workloads.
  • Design, document, and implement reliable ML infrastructure solutions.

Who You Are

  • 6+ years of hands-on experience in production ML infrastructure using Python, Go, or similar.
  • Knowledgeable in deep learning, algorithms, and open-source tools such as Huggingface, Ray, PyTorch, or TensorFlow.
  • Experienced with distributed training and Kubernetes management.
  • Familiar with data processing for ML and agile software development.

Where You'll Be

This role is based in Toronto, Canada with a flexible work arrangement that allows remote work with occasional in-person meetings.

Key skills/competency

  • Machine Learning
  • Python
  • Go
  • Kubernetes
  • TensorFlow
  • PyTorch
  • Huggingface
  • DevOps
  • Distributed Training
  • Agile

How to Get Hired at Spotify

🎯 Tips for Getting Hired

  • Customize your resume: Highlight ML platform and Kubernetes experience.
  • Research Spotify: Understand their AI/ML initiatives and culture.
  • Showcase projects: Present scalable ML infrastructure achievements.
  • Practice technical interviews: Focus on Python, Go, and Kubernetes questions.

📝 Interview Preparation Advice

Technical Preparation

Review Python and Go code examples.
Practice Kubernetes cluster management tasks.
Study ML frameworks and distributed training.
Revisit production ML deployment case studies.

Behavioral Questions

Describe project collaboration experiences.
Explain problem-solving under pressure.
Discuss adapting to new technologies.
Share teamwork and feedback examples.

Frequently Asked Questions