Senior Machine Learning Engineer, Recommendations (Experience)
at SoundCloud
- Seniority
- Senior
- Location
- Berlin, London
- Posted
- 7d ago
pythonjavascaladynamodbawsgcpkubernetesdockersparkairflowtensorflowpytorch
at SoundCloud
<div><span style="font-size: 10pt; font-family: helvetica, arial, sans-serif;">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.</span></div> <div> <div> <p><span style="font-family: helvetica, arial, sans-serif; font-size: 10pt;">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. </span></p> <p><span style="font-family: helvetica, arial, sans-serif; font-size: 10pt;">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.</span></p> </div> <p><span style="font-size: 10pt; font-family: helvetica, arial, sans-serif;"><strong>Key Responsibilities: </strong></span></p> <ul> <li style="font-family: helvetica, arial, sans-serif; font-size: 10pt;" data-section-id="57hkjq" data-start="2354" data-end="2438"><span style="font-family: helvetica, arial, sans-serif; font-size: 10pt;">Develop, test, and productionize ML and LLM-based systems serving real users</span></li> <li style="font-family: helvetica, arial, sans-serif; font-size: 10pt;" data-section-id="136jvff" data-start="2439" data-end="2536"><span style="font-family: helvetica, arial, sans-serif; font-size: 10pt;">Design and build end-to-end ML pipelines, including data, features, training, and serving</span></li> <li style="font-size: 10pt; font-family: helvetica, arial, sans-serif;"><span style="font-size: 10pt; font-family: helvetica, arial, sans-serif;">Make technical decisions considering cost, latency, complexity, and maintainability </span></li> <li style="font-size: 10pt; font-family: helvetica, arial, sans-serif;"><span style="font-size: 10pt; font-family: helvetica, arial, sans-serif;">Navigate distributed systems (BigQuery, BigTable, Airflow, DynamoDB) to build reliable, scalable solutions</span></li> <li style="font-size: 10pt; font-family: helvetica, arial, sans-serif;"><span style="font-size: 10pt; font-family: helvetica, arial, sans-serif;">Set up monitoring, A/B testing, and metrics frameworks to measure real user impact</span></li> <li style="font-size: 10pt; font-family: helvetica, arial, sans-serif;"><span style="font-size: 10pt; font-family: helvetica, arial, sans-serif;">Debug complex issues across data pipelines, ML models, and distributed systems</span></li> <li style="font-size: 10pt; font-family: helvetica, arial, sans-serif;"><span style="font-size: 10pt; font-family: helvetica, arial, sans-serif;">Contribute to technical strategy and team best practices</span></li> <li style="font-size: 10pt; font-family: helvetica, arial, sans-serif;"><span style="font-size: 10pt; font-family: helvetica, arial, sans-serif;">Leverage agentic workflows and AI-assisted engineering as a force multiplier to work at 10x the speed of traditional methods</span></li> </ul> <p><span style="font-size: 10pt; font-family: helvetica, arial, sans-serif;"><strong>Experience and Background:</strong></span></p> <ul> <li style="font-family: helvetica, arial, sans-serif; font-size: 10pt;"><span style="font-family: helvetica, arial, sans-serif; font-size: 10pt;">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</span></li> <li style="font-family: helvetica, arial, sans-serif; font-size: 10pt;"><span style="font-family: helvetica, arial, sans-serif; font-size: 10pt;">4+ years of software engineering experience - you write production code, not just notebooks</span></li> <li style="font-family: helvetica, arial, sans-serif; font-size: 10pt;"><span style="font-family: helvetica, arial, sans-serif; font-size: 10pt;">Strong Python and Scala (or Java/JVM) skills, with experience writing scalable, production code</span></li> <li style="font-family: helvetica, arial, sans-serif; font-size: 10pt;" data-section-id="ftu2lh" data-start="640" data-end="740"><span style="font-family: helvetica, arial, sans-serif; font-size: 10pt;">Experience building and deploying ML models end-to-end (data, training, serving, monitoring)</span></li> <li style="font-family: helvetica, arial, sans-serif; font-size: 10pt;"><span style="font-family: helvetica, arial, sans-serif; font-size: 10pt;">Experience building and deploying LLM-based features in production</span></li> <li style="font-family: helvetica, arial, sans-serif; font-size: 10pt;"><span style="font-family: helvetica, arial, sans-serif; font-size: 10pt;">Familiarity with integrating LLMs into ML systems (e.g. retrieval-augmented generation, model serving)</span></li> <li style="font-family: helvetica, arial, sans-serif; font-size: 10pt;"><span style="font-family: helvetica, arial, sans-serif; font-size: 10pt;">Understanding of shared ML architecture across domains (e.g. search and recommendations)</span></li> <li style="font-family: helvetica, arial, sans-serif; font-size: 10pt;"><span style="font-family: helvetica, arial, sans-serif; font-size: 1