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 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 (BigQuery, 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 TensorFlow or PyTorch
Benefits:
Not located in Berlin? No worries, we offer extensive relocation support including allowances, one way flights, temporary accommodation and, by partnering with Expath, on the ground support on arrival
Interested in a gym membership, photography course or book? We have a Creativity and Wellness benefit! - Employee Equity Plan
Generous professional development allowance
Flexible vacation and public holiday policy where you can take up to 35 days of PTO annually
We offer free German courses at beginning, intermediate and advanced
Various snacks, goodies, and 2 free lunches weekly when at the office