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- Tasks: Develop and implement ML systems to enhance user experience on Spotify.
- Company: Spotify is the world's leading audio streaming service, revolutionising music listening since 2008.
- Benefits: Enjoy flexible working options and a culture that values diversity and inclusivity.
- Why this job: Join a passionate team shaping the future of music recommendations for millions of users.
- Qualifications: Experience in applied machine learning and proficiency in Python required; familiarity with cloud platforms preferred.
- Other info: Remote work available within EMEA, excluding France; commitment to accessible recruitment.
The predicted salary is between 42000 - 84000 £ per year.
The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.
We are looking for a Machine Learning Engineer (MLE II) to join our product area of hardworking engineers that are passionate about connecting new and emerging creators with users via recommendation algorithms. As an integral part of the squad, you will collaborate with engineers, research scientists, and data engineers in prototyping and productizing state-of-the-art ML models that allow us to find the right audience for content that is strategically important, such as fresh or timely content.
What You’ll Do
- Develop and implement production systems that enrich and improve our listeners’ experience on the platform
- Contribute to designing, building, evaluating, shipping, and refining Spotify’s product by hands-on ML development
- Help drive optimization, testing, and tooling to improve quality of our recommendations
- Perform data analysis to establish baselines and inform product decisions
- Collaborate with a cross functional agile team spanning tech research, data science, product management, and engineering to build new technologies and features
- Stay up-to-date on the latest machine learning algorithms and techniques
Who You Are
- You have professional experience in applied machine learning
- Extensive experience working in a product and data-driven environment (Python, Scala, Java, SQL, or C++, with Python experience required) and cloud platforms (GCP or AWS)
- You have some hands-on experience implementing or prototyping machine learning systems at scale
- You have experience architecting data pipelines and are self-sufficient in getting the data you need to build and evaluate models, using tools like Dataflow, Apache Beam, or Spark
- You care about agile software processes, data-driven development, reliability, and disciplined experimentation
- You have experience and passion for fostering collaborative teams
- Experience with TensorFlow, pyTorch, and/or other scalable Machine learning frameworks
- Experience with building data pipelines and getting the data you need to build and evaluate your models, using tools like Apache Beam / Spark
Where You’ll Be
We offer you the flexibility to work where you work best! For this role, you can be within the EMEA region as long as we have a work location (excluding France due to on-call restrictions). This team operates within the Central European and GMT time zone for collaboration.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.
Machine Learning Engineer - Personalization (Basé à London) employer: Golden Bees
Contact Detail:
Golden Bees Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer - Personalization (Basé à London)
✨Tip Number 1
Familiarise yourself with Spotify's existing features like Blend and Discover Weekly. Understanding how these features work will help you demonstrate your knowledge of their product and how you can contribute to enhancing user experience through machine learning.
✨Tip Number 2
Showcase your hands-on experience with machine learning frameworks such as TensorFlow or PyTorch. Be prepared to discuss specific projects where you've implemented these technologies, as this will highlight your practical skills and readiness for the role.
✨Tip Number 3
Network with current or former employees of Spotify, especially those in the Personalization team. Engaging in conversations about their experiences can provide valuable insights into the company culture and expectations, which you can leverage during your application process.
✨Tip Number 4
Stay updated on the latest trends in machine learning and recommendation algorithms. Being able to discuss recent advancements or case studies during interviews will demonstrate your passion for the field and your commitment to continuous learning.
We think you need these skills to ace Machine Learning Engineer - Personalization (Basé à London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, particularly with Python and cloud platforms like GCP or AWS. Emphasise any hands-on experience you have with ML systems and data pipelines.
Craft a Compelling Cover Letter: In your cover letter, express your passion for music and podcasts, and how your skills can contribute to Spotify's mission. Mention specific projects or experiences that align with the role's requirements, especially in recommendation algorithms.
Showcase Your Technical Skills: Include specific examples of your work with machine learning frameworks such as TensorFlow or PyTorch. Discuss any projects where you've implemented or prototyped ML systems at scale, and how you approached data analysis.
Highlight Collaboration Experience: Since the role involves working in a cross-functional team, share examples of how you've successfully collaborated with engineers, data scientists, or product managers in previous roles. This will demonstrate your ability to thrive in an agile environment.
How to prepare for a job interview at Golden Bees
✨Showcase Your Machine Learning Experience
Be prepared to discuss your hands-on experience with machine learning systems. Highlight specific projects where you've implemented or prototyped models, especially those that had a significant impact on user experience.
✨Demonstrate Your Technical Skills
Make sure to brush up on your programming skills in Python, Scala, or Java. Be ready to answer technical questions and possibly solve coding challenges during the interview to showcase your proficiency.
✨Understand the Product and Its Users
Familiarise yourself with Spotify's features like Blend and Discover Weekly. Understanding how these features enhance user experience will help you articulate how your skills can contribute to the team’s goals.
✨Emphasise Collaboration and Agile Practices
Since the role involves working in a cross-functional agile team, be ready to discuss your experience in collaborative environments. Share examples of how you've worked with engineers, data scientists, and product managers to achieve common objectives.
