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 environment with mentorship opportunities and career advancement.
- Why this job: Make a real impact on user safety while working with cutting-edge multimodal technologies.
- Qualifications: Experience in production-grade ML systems and strong collaboration skills.
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.
Senior ML Engineer - Safety & Policy (Multimodal) employer: Spotify
At Spotify, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. As a Senior ML Engineer in our Policy & Safety team, you will have the opportunity to work on cutting-edge machine learning systems that ensure user safety while enjoying a supportive environment that encourages professional growth and mentorship. Located in a vibrant tech hub, we offer competitive benefits and a commitment to diversity, making Spotify a truly rewarding place to advance your career.
StudySmarter Expert Advice🤫
We think this is how you could land Senior ML Engineer - Safety & Policy (Multimodal)
✨Tip Number 1
Network like a pro! Reach out to folks in your industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Prepare for those interviews by brushing up on your technical skills and understanding the latest trends in ML. We recommend doing mock interviews with friends or using online platforms to get comfortable with the format.
✨Tip Number 3
Showcase your projects! Whether it’s a GitHub repo or a personal website, having a portfolio of your work can really set you apart. We love seeing how you’ve tackled real-world problems with your ML skills.
✨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, we’re always looking for passionate individuals who want to make a difference in the ML space.
We think you need these skills to ace Senior ML Engineer - Safety & Policy (Multimodal)
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 challenges in the past, especially with multimodal ML across different domains like text, audio, and video.
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 previous projects. We appreciate a good story that showcases your problem-solving skills.
Collaborate Like a Pro:Since we work closely with various teams, it’s important to demonstrate your ability to communicate effectively with both technical and non-technical partners. Share examples of how you’ve influenced technical direction and collaborated across teams in your past roles.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it shows you’re serious about joining our team!
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 scale, 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 cohesive models, especially in safety and policy contexts.
✨Communicate Clearly with All Stakeholders
Practice explaining complex technical concepts in simple terms. You’ll need to demonstrate your ability to collaborate with both technical and non-technical partners, so think of examples where you've successfully navigated these conversations.
✨Embrace Ambiguity and Make Trade-offs
Be prepared to discuss how you've handled ambiguous situations in past projects. Share specific instances where you had to make thoughtful trade-offs between speed, quality, and risk, and how those decisions impacted the outcome.
