Senior Machine Learning Engineer - Recommendations

Senior Machine Learning Engineer, Recommendations Experience

Berlin, London Sound Cloud empowers artists and fans to connect and share through music. Founded in 2007, Sound Cloud is an artist-first platform empowering artists to build and grow their careers by providing them with the most progressive tools, services, and resources. With over 400+ million tracks from 40 million artists, the future of music is Sound Cloud.

We are looking for a Senior Machine Learning Engineer to join our Recommendations Experience team, focusing on building ML-powered features that directly improve personalization, engagement, and satisfaction for our users. While this is an MLE role, you'll bring strong engineering fundamentals and work across the full stack and end-to-end systems, from data pipelines to APIs to real-time serving, and everything in between.

The Recommendations team ships ML-powered features that connect 200M+ users with music they'll love.

You'll own features end-to-end: from understanding user needs with Product and Design, to architecting data pipelines processing billions of events, to building and shipping production ML systems that balance performance, cost, and user experience. This means working across Big Query (trillion-row datasets), Airflow orchestration, real-time serving infrastructure (Big Table), APIs, and constant collaboration with Product, Design, Engineering, and Platform teams.

Key Responsibilities:

• Develop, test, and product ionize ML and LLM-based systems serving real users
• Design and build end-to-end ML pipelines, including data, features, training, and serving
• Make technical decisions considering cost, latency, complexity, and maintainability
• Navigate distributed systems (Big Query, Big Table, Airflow, DynamoDB) to build reliable, scalable solutions
• Set up monitoring, A/B testing, and metrics frameworks to measure real user impact
• Debug complex issues across data pipelines, ML models, and distributed systems
• Contribute to technical strategy and team best practices
• Leverage agentic workflows and AI-assisted engineering as a force multiplier to work at 10x the speed of traditional methods

Experience and Background:

• 1-2+ years building ML systems in production - you understand the difference between a model that works in Jupyter and one that serves millions of users
• 4+ years of software engineering experience - you write production code, not just notebooks
• Strong Python and Scala (or Java/JVM) skills, with experience writing scalable, production code
• Experience building and deploying ML models end-to-end (data, training, serving, monitoring)
• Experience building and deploying LLM-based features in production
• Familiarity with integrating LLMs into ML systems (e.g. retrieval-augmented generation, model serving)
• Understanding of shared ML architecture across domains (e.g. search and recommendations)
• Strong focus on data quality and correctness, and how upstream data impacts downstream models and user experience
• Strong SQL skills for massive datasets (Big Query, Spark)
• Cloud platform experience (AWS/GCP) and containerization (Docker, Kubernetes)
• Experience with distributed data processing and ETL pipelines (Airflow, Spark)
• Familiarity with ML frameworks such as Tensor Flow or Py Torch