At a Glance
- Tasks: Build and scale machine learning systems for content safety and policy enforcement.
- Company: Join Spotify's innovative Policy & Safety team, ensuring a safe user experience.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic role with mentorship opportunities and cross-functional teamwork.
- Why this job: Make a real impact on user safety while working with cutting-edge technology.
- Qualifications: Experience in machine learning systems and strong collaboration skills required.
The predicted salary is between 70000 - 90000 £ per year.
Requirements
- You have experience building and shipping production-grade machine learning systems at scale.
- You are experienced with ML evaluation, including dataset design, metrics, and model performance monitoring.
- You have worked with multimodal machine learning across text, audio, image, or video domains.
- You have experience with human-in-the-loop systems, active learning, or feedback-driven model improvement.
- You are comfortable translating complex requirements into technical solutions, including policy or regulatory constraints.
- You are experienced working across teams and influencing technical direction in large systems.
- You are comfortable navigating ambiguity and making thoughtful trade-offs between speed, quality, and risk.
- You communicate clearly and collaborate effectively with both technical and non-technical partners.
What the job involves
- The Policy & Safety team sits within the Content Platform domain and builds the systems that keep Spotify safe and trustworthy at scale.
- We own the infrastructure behind content moderation, including detection models, policy enforcement systems, compliance pipelines, and the safety-by-default platform.
- Our work is critical to every new content type and product experience—from messaging and comments to collaborative and emerging AI-driven features.
- We partner closely with Trust & Safety, Legal, and Public Affairs to ensure that safety is built into Spotify experiences from the start.
- Build and scale machine learning systems for proactive content detection, classification, and pre-publish safety scanning.
- Design and implement policy evaluation frameworks, including standardized datasets, offline and online metrics, and continuous improvement loops.
- Develop multimodal models that combine text, audio, image, and video signals for safety and policy enforcement.
- Architect feedback loops that turn reviewer input into structured training data for continuous model improvement.
- Translate regulatory requirements into scalable ML system designs, including accuracy and reporting expectations.
- Partner with cross-functional teams across Trust & Safety, Legal, Public Affairs, and Product to deliver safe user experiences.
- Drive technical direction in ambiguous problem spaces and contribute to long-term platform architecture.
- Mentor and support other machine learning engineers, helping grow technical capability across the team.
Staff Machine Learning Engineer (Safety & Policy) in London employer: Spotify
At Spotify, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. As a Staff Machine Learning Engineer in our Policy & Safety team, you will have the opportunity to work on cutting-edge technology that ensures user safety while also benefiting from extensive professional growth opportunities and mentorship. Our commitment to diversity, inclusion, and employee well-being, combined with our vibrant office environment, makes Spotify a truly rewarding place to advance your career.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Machine Learning Engineer (Safety & Policy) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already at Spotify or similar companies. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving multimodal systems. This is your chance to demonstrate your expertise beyond the CV.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts clearly, as you'll need to communicate effectively with both techies and non-techies.
✨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 StudySmarter.
We think you need these skills to ace Staff Machine Learning Engineer (Safety & Policy) in London
Some tips for your application 🫡
Show Off Your Experience:Make sure to highlight your experience with building and shipping production-grade machine learning systems. We want to see how you've tackled real-world challenges, so don’t hold back on the details!
Be Clear and Concise:When translating complex requirements into technical solutions, clarity is key. Use straightforward language to explain your thought process and how you’ve navigated ambiguity in past projects.
Collaborate Like a Pro:Since we work closely with various teams, it’s important to showcase your collaboration skills. Share examples of how you’ve effectively communicated with both technical and non-technical partners in your previous roles.
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’s super easy!
How to prepare for a job interview at Spotify
✨Know Your ML Systems Inside Out
Make sure you can talk confidently about your experience building and shipping production-grade machine learning systems. Be ready to discuss specific projects where you've tackled challenges related to model performance monitoring and evaluation metrics.
✨Showcase Your Multimodal Expertise
Prepare examples of how you've worked with multimodal machine learning across different domains like text, audio, and video. Highlight any innovative approaches you've taken to integrate these modalities into your projects.
✨Communicate Clearly About Complex Requirements
Practice explaining complex technical concepts in simple terms. You’ll need to demonstrate your ability to translate regulatory constraints into actionable solutions, so think of examples where you've successfully navigated ambiguity in your past work.
✨Collaborate and Influence Across Teams
Be ready to discuss your experience working with cross-functional teams. Share stories that illustrate how you've influenced technical direction and collaborated effectively with both technical and non-technical partners to achieve project goals.
