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) 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)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Spotify or similar companies. Attend meetups, webinars, or even online forums where you can chat about machine learning and safety policies. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving multimodal systems. Share your work on platforms like GitHub or even your own website. This gives potential employers a taste of what you can do before they even meet you.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss how you've tackled ambiguity in past projects and how you’ve collaborated with cross-functional teams. Practising common interview questions can help you feel more confident when it’s your turn to shine.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at StudySmarter. So, get that application in and let’s make some magic happen!
We think you need these skills to ace Staff Machine Learning Engineer (Safety & Policy)
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with production-grade machine learning systems. We want to see how you've tackled challenges in ML evaluation and multimodal projects, so don’t hold back on those details!
Showcase Collaboration Skills:Since we work closely with various teams, it’s crucial to demonstrate your ability to communicate and collaborate effectively. Share examples of how you’ve influenced technical direction or navigated ambiguity in past projects.
Be Clear and Concise:When writing your application, clarity is key! Use straightforward language to explain complex concepts, especially around policy and regulatory constraints. We appreciate a well-structured application that gets straight to the point.
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 Spotify
✨Showcase Your ML Experience
Make sure to highlight your experience with building and shipping production-grade machine learning systems. Be ready to discuss specific projects where you’ve designed datasets, monitored model performance, and tackled challenges in multimodal machine learning.
✨Communicate Clearly
Since the role involves collaborating with both technical and non-technical partners, practice explaining complex concepts in simple terms. Use examples from your past work to demonstrate how you’ve successfully communicated across teams.
✨Prepare for Ambiguity
Expect questions about navigating ambiguity and making trade-offs. Think of scenarios where you had to balance speed, quality, and risk, and be prepared to discuss your thought process and decision-making strategies.
✨Demonstrate Cross-Functional Collaboration
Be ready to talk about your experience working with diverse teams, especially in areas like Trust & Safety or Legal. Share examples of how you influenced technical direction and contributed to long-term platform architecture in previous roles.
