Senior Machine Learning Engineer - Recommendations (Experience)

Description

The company was founded in 2007 and runs a music platform where artists and fans connect and share, with over 400+ million tracks from 40 million artists.

You would join the Recommendations Experience team as a Senior Machine Learning Engineer, building ML-powered features that improve personalization, engagement and satisfaction for users.

This is an MLE role, but you bring solid engineering foundations and work across the stack, from data pipelines and APIs to real-time serving.

The team ships ML features that match 200M+ users with music they will want.

You take a feature from user research with Product and Design through to a production ML system that balances performance, cost and user experience.

The team invests in its people through mentorship, workshops and learning opportunities.

Responsibilities

• Build, test and put into production ML and LLM systems used by real users.
• Design end-to-end ML pipelines covering data, features, training and serving.
• Architect pipelines that process billions of events across trillion-row BigQuery datasets.
• Weigh cost, latency, complexity and maintainability when making technical choices.
• Build reliable, scalable solutions on distributed systems such as BigQuery, BigTable, Airflow and DynamoDB.
• Set up monitoring, A/B tests and metrics to measure real user impact.
• Debug hard problems across data pipelines, ML models and distributed systems.
• Shape technical strategy and team practices.
• Use agentic workflows and AI-assisted engineering to work at 10x traditional speed.
• Collaborate closely with Product, Design, Engineering and Platform teams.

Requirements

• 1-2+ years building ML systems in production, well beyond prototypes in notebooks.
• 4+ years of software engineering experience, writing production code.
• Strong Python and Scala, or Java/JVM, with experience writing scalable production code.
• Experience deploying ML models end-to-end, covering data, training, serving and monitoring.
• Experience building and running LLM-based features in production.
• Familiarity with integrating LLMs into ML systems, for example retrieval-augmented generation and model serving.
• Understanding of ML architecture shared across domains such as search and recommendations.
• Commitment to data quality and correctness, and awareness of how upstream data affects models and users.
• Strong SQL for massive datasets in BigQuery or Spark.
• Cloud experience on AWS or GCP and containerisation with Docker and Kubernetes.
• Experience with distributed data processing and ETL pipelines using Airflow or Spark.
• Familiarity with ML frameworks such as TensorFlow or PyTorch.

Conditions

Offices in New York, Los Angeles, Berlin and London.

Flexible setup: you can collaborate in person at the offices or work from home.

The company is committed to diversity, equity and inclusion and values every voice.

Benefits

• Relocation support for those not based in Berlin: allowances, one-way flights, temporary accommodation and on-the-ground help on arrival through partner Startcon.
• Creativity and Wellness benefit covering items such as gym memberships, photography courses or books.
• Employee Equity Plan.
• Professional development allowance.
• Flexible vacation and public holiday policy, with up to 35 days of PTO a year.
• Free German courses at beginner, intermediate and advanced levels.
• Snacks, treats and 2 free lunches a week when on site.