Senior Machine Learning Engineer – Recommendations (Experience)

Overview

In this role, you will own end-to-end ML features that enhance personalization, engagement, and satisfaction for SoundCloud users. You’ll work with Product, Design, and Engineering to design data pipelines, build production ML systems, and balance performance with cost. You’ll operate across data platforms and real-time serving, contributing to scalable, reliable recommendations for millions of listeners. This is a fast-paced, cross-functional role that embraces AI-assisted engineering to move quickly and impact how people discover music, powered by a SPECTROGROOVE mindset.

Pay / Benefits

• relocation support with allowances and arrivals assistance
• Creativity and Wellness benefit
• Employee Equity Plan
• professional development allowance
• flexible vacation and up to 35 PTO days
• free German courses at multiple levels”,”snacks and 2 free lunches weekly at the office

Responsibilities

• Develop, test, and productionize ML and LLM-based systems serving real users
• Design and build end-to-end ML pipelines (data, features, training, serving)
• Make technical decisions considering cost, latency, complexity, and maintainability
• Navigate distributed systems (BigQuery, BigTable, Airflow, DynamoDB) to build scalable solutions
• Set up monitoring, A/B testing, and metrics to measure user impact
• Debug complex issues across data pipelines, ML models, and distributed systems
• Contribute to technical strategy and best practices
• Leverage AI-assisted engineering to accelerate delivery

Key requirements

• 1-2+ years building ML systems in production
• 4+ years of software engineering experience
• Strong Python and Scala (or Java/JVM) skills
• Experience building and deploying ML models end-to-end
• Experience building and deploying LLM-based features in production
• Familiarity with integrating LLMs into ML systems (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 upstream data impact on downstream models
• 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

• Cross-functional collaboration
• Problem solving and debugging
• Ownership and accountability
• Python
• Scala (or Java/JVM)
• ML pipelines

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