Senior Machine Learning Engineer, Recommendations (Experience)

Job description

SoundCloud empowers artists and fans to connect and share through music. Founded in 2007, SoundCloud 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 SoundCloud. 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 BigQuery (trillion-row datasets), Airflow orchestration, real-time serving infrastructure (BigTable), APIs, and constant collaboration with Product, Design, Engineering, and Platform teams. Key Responsibilities: Develop, test, and productionize 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 (BigQuery, BigTable, 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 wr…