Staff Machine Learning Engineer - Policy & Safety

Posted 12 Days Ago
Be an Early Applicant
2 Locations
Hybrid
Mid level
Music
The Role
The Staff Machine Learning Engineer builds and scales ML systems for content detection and policy enforcement, collaborating with various teams to ensure safety and compliance in Spotify's user experiences.
Summary Generated by Built In

We design Spotify’s consumer experience—end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints—from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify.

About the Team
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.

What You Will Do

  • 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

Who You Are

  • 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

Where You Will Be

  • This role is based in London or Stockholm
  • We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.

Skills Required

  • Experience building and shipping production-grade machine learning systems at scale
  • Experience with ML evaluation, dataset design, metrics, and model performance monitoring
  • Experience with multimodal machine learning across various domains
  • Experience with human-in-the-loop systems or feedback-driven model improvement
  • Skill in translating complex requirements into technical solutions
  • Experience working across teams and influencing technical direction
  • Ability to navigate ambiguity and make trade-offs
  • Clear communication skills with technical and non-technical partners

Spotify Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Spotify and has not been reviewed or approved by Spotify.

  • Flexible Benefits Employees consistently praise the total compensation package beyond base salary, highlighting a mix of RSUs, cash incentives, and stipends alongside core pay. The package is described as flexible and customizable through equity choices (e.g., RSUs, options, cash) that can be tailored for long-term wealth building.
  • Leave & Time Off Breadth Time-off offerings are repeatedly highlighted as substantial, including generous vacation, paid sick days, volunteer time, and flexible holidays. These policies are framed as a meaningful part of the overall rewards experience beyond salary.
  • Healthcare Strength Health coverage is portrayed as comprehensive, spanning medical, dental, vision, life insurance, disability coverage, and mental health support. Additional employer contributions to HSAs are cited as strengthening the overall health and wellness value proposition.

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The Company
HQ: Stockholm
9,574 Employees
Year Founded: 2006

What We Do

Spotify transformed music listening forever when it launched in Sweden in 2008. Discover, manage and share over 50m tracks for free, or upgrade to Spotify Premium to access exclusive features including offline mode, improved sound quality, and an ad-free music listening experience.

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