Senior Machine Learning Engineer, Recommendations (Product)
External
Soundcloud71 · NYPrepare for this interview
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About the role
We are a multinational company with offices in the US (New York and Los Angeles), Germany (Berlin), and the UK (London) We provide a flexible work culture that offers the opportunity to collaborate and connect in person at our offices as well as accommodating work from home We are deeply committed to ensuring diversity, equity and inclusion at all levels of our organization and fostering a community where everyone's voice, perspective and experience is respected and heard We believe a strong team is made by investing in employees through mentorship, workshops and enrichment opportunities
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
- The salary range for this role is $165,000 - $195,000 annually. The final salary offered will be determined based on relative experience, skills, internal equity, and location. We also offer a generous total rewards program - read more about additional benefits and perks below!
Benefits
Additional Information
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.
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