At a Glance
- Tasks: Build and scale machine learning systems for content understanding across audio, video, text, and images.
- Company: Join Spotify's innovative Content Platform team shaping the future of audio experiences.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment focused on innovation and user experience.
- Why this job: Make a real impact on millions of listeners and creators with cutting-edge AI technology.
- Qualifications: Experience in deploying ML systems and working with large datasets is essential.
The predicted salary is between 70000 - 90000 € per year.
The Content Platform team powers the full lifecycle of content across music, podcasts, audiobooks, and emerging formats at Spotify. We ensure that everything from licensed catalog to user-generated content is trusted, safe, and high quality for millions of listeners worldwide. Our systems are responsible for how content is ingested, understood, enriched, governed, and distributed across the platform. As the scale and diversity of content continues to grow - driven by advances in AI and new creation tools - we're building intelligent systems that can evaluate, manage, and route content reliably at global scale.
We're seeking a Staff Machine Learning Engineer to build and scale foundational ML systems that power content understanding across Spotify. In this role, you'll work on systems that generate deep, machine-readable understanding of content across audio, video, text, and images - enabling automation, improving quality, and unlocking new product experiences. This work is central to delivering safe, high-quality, and differentiated experiences for millions of listeners and creators worldwide.
What You Will Do
- Build and scale machine learning systems that generate deep understanding of content across modalities
- Develop models for classification, tagging, semantic understanding, and content enrichment
- Create high quality content enrichment at scale using LLMs and agentic systems
- Design systems that make content intelligence signals available to downstream teams and products
- Improve automation for content quality, safety, and metadata enrichment at scale
- Collaborate with product, policy, and engineering teams to translate content intelligence into user impact
- Contribute to evaluation frameworks, data pipelines and annotation systems
- Support rapid experimentation to prototype and launch new types of content signals
- Help improve system reliability, scalability and performance across large datasets
Qualifications
- You have experience building and deploying machine learning systems in production
- You are comfortable working with ML frameworks such as PyTorch, TensorFlow, or similar
- You have experience working with large datasets and care about data quality and evaluation
- You are interested or have worked with multimodal machine learning
- You understand how to design systems that balance automation with quality and user experience
- You are comfortable working on complex problems with evolving requirements
- You think in systems and understand how models connect to product outcomes
- You communicate clearly and work well across technical and non-technical teams
Staff Machine Learning Engineer - Content Intelligence employer: Spotify
Spotify is an exceptional employer that fosters a dynamic and innovative work culture, particularly for the Staff Machine Learning Engineer role within the Content Platform team. Employees benefit from a collaborative environment that encourages creativity and experimentation, alongside opportunities for professional growth in the rapidly evolving field of AI and content intelligence. With a commitment to high-quality content and user experience, Spotify offers a unique chance to make a meaningful impact on millions of listeners and creators worldwide.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Machine Learning Engineer - Content Intelligence
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Spotify or similar companies. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's a GitHub repo or a personal website, let your work speak for itself. We love seeing what you can do!
✨Tip Number 3
Prepare for interviews by brushing up on your ML knowledge and problem-solving skills. Practice common interview questions and think about how your experience aligns with the role. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining our team at StudySmarter.
We think you need these skills to ace Staff Machine Learning Engineer - Content Intelligence
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Staff Machine Learning Engineer role. Highlight your experience with ML frameworks like PyTorch or TensorFlow, and don’t forget to mention any work with large datasets!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about content intelligence and how your background makes you a perfect fit for our team. Be sure to mention specific projects or achievements that relate to the job.
Showcase Your Projects:If you've worked on relevant projects, whether in a professional setting or as personal endeavours, make sure to include them. We love seeing practical applications of your skills, especially those that demonstrate your ability to handle complex problems.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re genuinely interested in joining our team at StudySmarter!
How to prepare for a job interview at Spotify
✨Know Your ML Frameworks
Make sure you brush up on your experience with ML frameworks like PyTorch and TensorFlow. Be ready to discuss specific projects where you've used these tools, as well as any challenges you faced and how you overcame them.
✨Showcase Your Data Quality Focus
Since the role involves working with large datasets, be prepared to talk about your approach to data quality and evaluation. Share examples of how you've ensured high-quality data in past projects and the impact it had on your models.
✨Understand Multimodal Machine Learning
Familiarise yourself with multimodal machine learning concepts, as this is a key aspect of the role. Be ready to explain how you would approach building systems that understand content across different formats like audio, video, and text.
✨Communicate Across Teams
Highlight your ability to collaborate with both technical and non-technical teams. Prepare examples of how you've successfully translated complex technical concepts into user-friendly language, ensuring everyone is on the same page.
