National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Machine Learning Engineer, Content Understanding

Spotify AB
London
1 month ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer Trainer

Machine Learning Engineer Trainer

Senior MLOps/GenAI Infrastructure Engineer

Senior AI | Machine Learning Engineer

Senior AI | Machine Learning Engineer

Senior Data Engineer

Delivering the best Spotify experience possible. To as many people as possible. In as many moments as possible. That’s what the Experience team is all about. We use our deep understanding of consumer expectations to enrich the lives of millions of our users all over the world, bringing the music and audio they love to the devices, apps and platforms they use every day. Know what our users want? Join us and help Spotify give it to them.

Location

  • London

Job type

Permanent

As a Machine Learning Engineer in our Content Understanding teams, you will help define and build ML deployed at scale in support of a broad range of use cases driving value in media and catalog understanding.

Here are some examples of the work you may support:

  • Audio fingerprinting to understand what music is played in podcasts enabling musicians to get royalties
  • Video and image tagging to understand what is happening in any video on Spotify for moderation and recommendations
  • Audiobook Author attribution using graph ML approaches for search and recommendations
  • Categorizing tracks in the catalog to know which are functional content or music tracks leveraged in royalty calculations and in search and recommendations

Our teams are composed of product, machine learning, data and backend engineers, and subject matter experts who average 11 years behind the scenes in the music industry.

We are looking for a Machine Learning Engineer to help us define and build Spotify’s capabilities in this area. Our team expands the state of the art in AI-based machine technology, which enables intelligent, efficient, and intuitive ways to search, re-use, explore or process metadata. You will use world-class engineering and machine learning techniques on real-world, internal, and external big data to directly impact the evolution of our music catalog.

What Youll Do

  • Build 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
  • Prototype new approaches and production-ize solutions at scale for our hundreds of millions of active users
  • Help drive optimization, testing, and tooling to improve quality
  • Perform data analysis to establish baselines and inform product decisions
  • Collaborate with a cross functional agile team spanning design, data science, product management, and engineering to build new technologies and features

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 Google Cloud Platform is a plus
  • Experience with building data pipelines and getting the data you need to build and evaluate your models, using tools like Apache Beam / Spark is a plus

Where Youll Be

  • This role is based in London (UK)
  • We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home. We ask that you come in 3 times per week.

Extensive learning opportunities, through our dedicated team, GreenHouse.

Flexible share incentives letting you choose how you share in our success.

Global parental leave, six months off - fully paid - for all new parents.

All The Feels, our employee assistance program and self-care hub.

Flexible public holidays, swap days off according to your values and beliefs.

Learn about life at Spotify

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.

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 with a community of more than 500 million users.

J-18808-Ljbffr

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.