Senior Machine Learning Engineer, Recommendations (Experience)

SoundCloud•New York, NY

About The Position

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.

Requirements

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 Scala knowledge or closely related JVM languages, with strong functional programming experience. Python and Go are a Plus.
Deep SQL skills for massive datasets (BigQuery, Spark)
Cloud platform experience (AWS/GCP) and containerization (Docker, Kubernetes)
Familiarity with TensorFlow, PyTorch, or similar frameworks
Experience with distributed data processing and ETL pipelines (Airflow, Spark)
Understanding of data consistency patterns, eventual consistency, and the trade-offs
You can debug issues across multiple systems and data sources

Responsibilities

Develop, test, and productionize ML models
Make technical decisions considering cost, latency, complexity, and maintainability
Navigate distributed systems (BigQuery, BigTable, Airflow, DynamoDB) to build reliable, scalable solutions
Design and implement data pipelines, feature engineering, model training, and serving infrastructure
Write technical RFCs and communicate trade-offs to diverse stakeholders
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
Champion maintainable code over clever code - write clear, testable Scala/Python that your teammates can modify
Share knowledge through documentation, code reviews, and mentoring
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

Benefits

Comprehensive health benefits including medical, dental, and vision plans, as well as mental health resources
Robust 401k program
Employee Equity Plan
Generous professional development allowance
Interested in a gym membership, photography course or book? We have a Creativity and Wellness benefit!
Flexible vacation and public holiday policy where you can take up to 35 days of PTO annually
16 paid weeks for all parents (birthing and non-birthing), regardless of gender, to welcome newborns, adopted and foster children
Various snacks, goodies, and 2 free lunches weekly when at the office

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