Senior Machine Learning Engineer, Recommendations (Product)

Senior Machine Learning Engineer, Recommendations (Product)

New York

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 200 million+ 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, Airflow, real-time serving infrastructure, 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 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

Experience and Background

• 1–2+ years building ML systems in production – understand the difference between a model that works in Jupyter and one that serves millions of users
• 4+ years of software engineering experience – 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 (eg. search and recommendations)
• Strong focus on data quality and correctness, and how upstream data impacts downstream models and user experience
• 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

Salary range: $165,000 – $195,000 annually. Final salary will be determined based on experience, skills, internal equity, and location. SoundCloud offers a generous total rewards program.

Benefits

• Comprehensive health benefits including medical, dental, and vision plans, and mental health resources
• Robust 401k program
• Employee Equity Plan
• Generous professional development allowance
• Creativity and Wellness benefit (e.g., gym membership, photography course)
• Flexible vacation and public holiday policy – up to 35 days of PTO annually
• 16 paid weeks for all parents, regardless of gender
• Various snacks, goodies, and 2 free lunches weekly at the office

Diversity, Equity and Inclusion. SoundCloud is for everyone. Diversity and open expression are fundamental to our organization; we are dedicated to creating an inclusive environment for everyone, regardless of gender identity, sexual orientation, race, ethnicity, migration background, national origin, age, disability status, or caregiver status.