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

Nominate & Attend

Machine Learning Engineer - Content and Catalog Management (London)

Spotify
London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer - Bioimage Data & Agentic Systems

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer Trainer

Machine Learning Engineer - Up to £150k + Equity

The Catalog and Content Management (CoCaM) team works at the heart of the Content Platform R&D studio, the central point for the ingestion, distribution, management, knowledge and growth of all content you experience through Spotify products. In CoCaM we drive the management of content and make decisions that impact the whole of Spotify on all content’s appropriateness, availability, quality and accuracy. Through reactive and proactive reporting mechanisms we use the knowledge of Content Platform and apply platform & business policy with content, user, financial and experiential context to make and store a decision best for Creators, Consumers and Spotify.

This is an outstanding opportunity to contribute to the development and application of ML within our content and catalogue management platform. You’ll be at the forefront of driving impactful solutions, while collaborating within a dynamic and supportive team environment.

What You'll Do

  • Drive the full lifecycle of ML solutions for CoCaM services, including research, design, development, evaluation, and deployment.
  • Manage Machine Learning projects ranging from Supervised Learning, to Reinforcement Learning, to LLMs.
  • Optimize and monitor deployed ML model performance, implementing improvements based on analysis.
  • Document and standardize ML processes, pipelines, and model specifications.
  • Collaborate with cross-functional teams spanning research, engineering, data science, product managers and other stakeholders to understand business needs and identify opportunities for ML applications.
  • Work closely with engineering teams to integrate ML models into existing systems and workflows.
  • Be an active participant of a group of machine learning engineers, staying updated with the latest advancements, participating in code reviews, and contributing to knowledge sharing across the team.

Who You Are

  • 2+ years of hands-on experience in developing and deploying machine learning models in a production environment.
  • Practical experience in implementing ML systems using languages like Python or Scala and are familiar with relevant ML libraries and frameworks (e.g., TensorFlow or PyTorch).
  • Solid understanding of various machine learning algorithms (e.g., classification, regression, clustering) and their practical applications.
  • Proficient in data manipulation and analysis using tools like SQL and Pandas.
  • Broad ML skillset and are happy to work on all aspects of ML problems. Not only modeling, but also feature work in data pipelines, some implementation in data pipeline workflows, experimentation setup and analysis.
  • Experience with model evaluation metrics and techniques for ensuring model quality and generalization.
  • Experience with cloud platforms (e.g., GCP, AWS, Azure) and their ML services.
  • Comfortable communicating technical concepts clearly and effectively within the team and with non-technical stakeholders.
  • Proactive problem-solver with a strong sense of ownership and a drive to learn.

Where You'll 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.


#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.