At a Glance
- Tasks: Build and scale ML systems for content understanding across audio, video, text, and images.
- Company: Join Spotify, a leader in music and content innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on innovation and collaboration.
- Why this job: Make a real impact on millions of listeners and creators with cutting-edge technology.
- Qualifications: Experience with ML frameworks and large datasets; strong communication skills.
The predicted salary is between 70000 - 90000 € per year.
Requirements
- 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 in 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.
What the job involves
- 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.
- 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.
Staff Machine Learning Engineer (Content Intelligence) in London employer: Deepstreamtech
At Spotify, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. As a Staff Machine Learning Engineer, you'll have the opportunity to work with cutting-edge technologies in a vibrant environment that values employee growth and development, while contributing to meaningful projects that impact millions of users globally. Our commitment to diversity, inclusion, and employee well-being ensures that you will thrive both personally and professionally in our supportive community.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Machine Learning Engineer (Content Intelligence) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving PyTorch or TensorFlow. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your problem-solving skills. Be ready to discuss how you've tackled complex ML challenges in the past and how you balance automation with quality. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Staff Machine Learning Engineer (Content Intelligence) in London
Some tips for your application 🫡
Show Off Your Experience:Make sure to highlight your experience with building and deploying machine learning systems. We want to see how you've tackled complex problems and what frameworks you've used, like PyTorch or TensorFlow. Don't hold back on the details!
Data Quality Matters:Since we care about data quality and evaluation, share examples of how you've worked with large datasets. Talk about your approach to ensuring data integrity and how it impacted your projects. This will show us you understand the importance of quality in ML.
Connect the Dots:We love candidates who think in systems! Make sure to explain how your work connects to product outcomes. If you've designed systems that balance automation with user experience, let us know. It’s all about showing how your skills can drive impact.
Communicate Clearly:Since you'll be collaborating with both technical and non-technical teams, clear communication is key. Use your application to demonstrate how you've effectively communicated complex ideas in the past. We want to see that you can bridge the gap between different teams!
How to prepare for a job interview at Deepstreamtech
✨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, focusing on how you tackled challenges and improved system performance.
✨Showcase Your Data Quality Skills
Prepare examples that highlight your commitment to data quality and evaluation. Discuss how you've worked with large datasets in the past, and be ready to explain your approach to ensuring data integrity and accuracy in your models.
✨Think Multimodal
Since the role involves multimodal machine learning, think about any relevant experiences you have. Be prepared to talk about how you've integrated different types of data (like audio, video, and text) into your ML systems and the impact it had on user experience.
✨Communicate Across Teams
This position requires collaboration with both technical and non-technical teams. Prepare to share examples of how you've effectively communicated complex ideas to diverse audiences, ensuring everyone is on the same page and working towards common goals.
