Creandum

Spotify's Subscriptions Mission focuses on converting listeners into lifelong subscribers by delivering seamless, valuable experiences across pricing, packaging, and customer journeys. We build the systems and tools that power acquisition, retention, and overall subscription growth at scale. The Messaging Platform powers Spotify's communications to over a billion users - from push notifications to emails and in-app messages that connect listeners to the content they love. Within this space, the Paloma squad focuses on message optimization: deciding which message reaches which user, through which channel, and at what moment. We're evolving how messaging works at Spotify - moving from short-term optimization toward systems that understand long-term user journeys. By combining reinforcement learning approaches with deeper domain signals, we're expanding how machine learning shapes the entire messaging funnel. What You'll Do Design, build, and ship machine learning models that optimize messaging across push, email, and in-app channels Plan and run A/B experiments in a multi-objective environment, balancing conversion, engagement, retention, and reachability Contribute to reinforcement learning systems that optimize for long-term user outcomes rather than immediate interactions Partner with product managers, data scientists, and engineers to define what success looks like and how to measure it Own the full ML lifecycle, from data and modeling to deployment, monitoring, and iteration Integrate ML models with upstream systems, including domain value signals and opportunity generation frameworks Help shape the future of AI assisted development within the team, exploring how tools can accelerate experimentation and delivery Who You Are You have strong experience building and deploying machine learning models in production environments at scale You are comfortable translating business problems into ML solutions and discussing trade offs with cross functional partners You have worked on complex optimization problems such as ranking systems or multi objective decision making You bring hands on experience with PyTorch and distributed systems such as Ray or similar frameworks You understand experimentation deeply and can design reliable tests in environments with interacting metrics You are able to analyze results using approaches like causal inference or metric decomposition when needed You have experience with or curiosity about reinforcement learning and long term optimization systems You enjoy working across disciplines and navigating ambiguity while shaping strategy and direction Where You'll Be This role is based in London and Stockholm 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 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.

Creandum is looking for a skilled machine learning professional to optimize messaging at Spotify, steering the design and implementation of models across various channels. The role requires deep experience in building and deploying ML models, particularly in complex optimization scenarios. As part of a collaborative community, your contributions will shape AI-assisted development and enhance user communication experiences. This position is based in London with flexible work-from-home options.

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. You'll join the Horizon Product Area within the Sessions Studio, part of Spotify's Personalization Mission. This team focuses on inventing and evolving new listening experiences powered by emerging technologies. From AI DJ to promptable playlists and generated podcasts, we're exploring how agentic systems and generative AI can reshape how people interact with audio. You'll work at the intersection of product innovation and cutting edge machine learning to bring entirely new experiences to life for millions of listeners. What You'll Do Design, build, evaluate, and ship agentic based features and interactive experiences to bring our products to the next level Collaborate with cross functional teams spanning user research, design, data science, product management, and engineering to build new product features that advance our mission to connect artists and fans in personalized and useful ways Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization Actively contributed to a strong community of machine learning practitioners at Spotify Who You Are An experienced ML practitioner motivated to work on complex real-world problems in a fast-paced and collaborative environment Strong background in machine learning, natural language processing, and generative AI, with experience in applying theory to develop real-world applications Hands on expertise with implementing end-to-end production ML systems at scale. Experience with production LLM scale based systems is a plus Experience with incorporating human feedback to improve LLM based systems using technicals like DPO, KTO, and reinforcement fine-tuning Experience with designing end-to-end tech specs and modular architectures for ML frameworks in complex problem spaces in collaboration with product teams Experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, and cloud platforms like GCP or AWS Where You'll Be We offer you the flexibility to work where you work best! For this role, you can be within the European region as long as we have a work location. This team operates within the GMT/CET time zone for collaboration. Excluding France due to on call restrictions. 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.

We design Spotify's consumer experience-end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints-from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify. About the Team The Policy & Safety team sits within the Content Platform domain and builds the systems that keep Spotify safe and trustworthy at scale. We own the infrastructure behind content moderation, including detection models, policy enforcement systems, compliance pipelines, and the safety-by-default platform. Our work is critical to every new content type and product experience-from messaging and comments to collaborative and emerging AI-driven features. We partner closely with Trust & Safety, Legal, and Public Affairs to ensure that safety is built into Spotify experiences from the start. What You Will Do Build and scale machine learning systems for proactive content detection, classification, and pre publish safety scanning Design and implement policy evaluation frameworks, including standardized datasets, offline and online metrics, and continuous improvement loops Develop multimodal models that combine text, audio, image, and video signals for safety and policy enforcement Architect feedback loops that turn reviewer input into structured training data for continuous model improvement Translate regulatory requirements into scalable ML system designs, including accuracy and reporting expectations Partner with cross functional teams across Trust & Safety, Legal, Public Affairs, and Product to deliver safe user experiences Drive technical direction in ambiguous problem spaces and contribute to long term platform architecture Mentor and support other machine learning engineers, helping grow technical capability across the team Who You Are You have experience building and shipping production grade machine learning systems at scale You are experienced with ML evaluation, including dataset design, metrics, and model performance monitoring You have worked with multimodal machine learning across text, audio, image, or video domains You have experience with human in the loop systems, active learning, or feedback driven model improvement You are comfortable translating complex requirements into technical solutions, including policy or regulatory constraints You are experienced working across teams and influencing technical direction in large systems You are comfortable navigating ambiguity and making thoughtful trade offs between speed, quality, and risk You communicate clearly and collaborate effectively with both technical and non technical partners Where You Will Be This role is based in London or Stockholm 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.

Creandum is seeking a Machine Learning Engineer to build and scale innovative systems for content safety. This role involves developing machine learning models that handle various formats including text, audio, and video. The position is primarily based in London or Stockholm, offering flexibility to work from home with some in-person meetings. Ideal candidates are proficient in production-scale systems, and ML evaluation, and can navigate complex regulatory requirements effectively.

We design Spotify's consumer experience-end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints-from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify. 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 Who You Are 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 Where You Will Be This role is based in London or Stockholm 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.

Creandum is looking for a Staff Machine Learning Engineer to build and scale ML systems for content understanding across audio, video, text, and images. In this pivotal role, you will develop models to enhance content quality and safety, ensuring high-quality experiences for users. This position offers flexibility to work from home or in the London office, encouraging collaboration with product and engineering teams to drive impact.