At a Glance
- Tasks: Build ML-powered features to enhance user engagement and satisfaction.
- Company: Join SoundCloud, a leader in music innovation and inclusivity.
- Benefits: Relocation support, wellness benefits, equity plan, and generous PTO.
- Other info: Diverse and inclusive culture with excellent career growth opportunities.
- Why this job: Make a real impact on user experience with cutting-edge ML technology.
- Qualifications: 1-2 years of ML experience and strong software engineering skills required.
The predicted salary is between 60000 - 80000 ÂŁ per year.
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. You will 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.
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 (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
Benefits
- Relocation support including allowances, one‑way flights, temporary accommodation, and on‑the‑ground support on arrival
- Creativity and Wellness benefit (e.g. gym membership, photography course, book allowance)
- Employee Equity Plan
- Generous professional development allowance
- Flexible vacation and public holiday policy – up to 35 days of PTO annually
- Free German courses (beginning, intermediate, advanced)
- Various snacks, goodies, and two free lunches weekly when at the office
Diversity, Equity and Inclusion
SoundCloud is for everyone. Diversity and open expression are fundamental to our organization; they help us lead what’s next in music by understanding and empowering our creators and fans, no matter their identity. We acknowledge the challenges in the music industry, and strive to influence an inclusive culture where everyone can contribute respectfully and thrive, especially the historically marginalized communities that many of our creators, fans and SoundClouders identify with. 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 care‑giver status. At SoundCloud, you can find your community or elevate your allyship by joining a Diversity Resource Group. Diversity Resource Groups are employee‑organized groups focused on supporting and promoting the interests of a particular under‑represented community in order to build a more inclusive culture at SoundCloud. Anyone can join, whether you share the identity or strive to be an ally.
Senior Machine Learning Engineer, Recommendations (Product) in London employer: SoundCloud
Contact Detail:
SoundCloud Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer, Recommendations (Product) in London
✨Tip Number 1
Network like a pro! Reach out to current employees at SoundCloud or similar companies on LinkedIn. A friendly chat can give you insider info and might even lead to a referral, which is always a bonus!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those that highlight your experience with end-to-end systems. This will help you stand out and demonstrate your hands-on expertise.
✨Tip Number 3
Prepare for the technical interview by brushing up on your Python, Scala, and SQL skills. Practice coding challenges and system design questions that relate to building scalable ML systems. We want to see how you think!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining our team at SoundCloud.
We think you need these skills to ace Senior Machine Learning Engineer, Recommendations (Product) in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Senior Machine Learning Engineer role. Highlight your experience with ML systems, data pipelines, and any relevant projects that showcase your skills in Python or Scala.
Showcase Your Projects: Include specific examples of ML projects you've worked on, especially those that involved end-to-end development. We want to see how you’ve tackled real-world problems and the impact your work had on user experience.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language to describe your technical skills and experiences. Avoid jargon unless it's necessary, and make sure your passion for ML shines through!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at SoundCloud
✨Know Your ML Stuff
Make sure you brush up on your machine learning fundamentals, especially around building and deploying ML systems. Be ready to discuss your experience with end-to-end pipelines and how you've tackled real-world challenges in production.
✨Show Off Your Engineering Skills
This role requires strong engineering fundamentals, so be prepared to talk about your coding experience in Python or Scala. Bring examples of scalable production code you've written and be ready to explain your thought process behind technical decisions.
✨Understand the User Impact
Since this position focuses on improving user engagement and satisfaction, think about how your work has directly impacted users in the past. Be ready to discuss metrics, A/B testing, and how you've measured success in your previous projects.
✨Collaborate Like a Pro
Collaboration is key in this role, so highlight your experience working with cross-functional teams. Share examples of how you've partnered with product, design, and engineering teams to deliver successful ML features that meet user needs.