Senior Machine Learning Engineer - Policy & Safety

Team: Experience

Location: London, Stockholm, Gothenburg

Commitment: Permanent

Workplace Type: hybrid

What Youll Do

Design, build, and ship production-grade machine learning systems that power content safety and policy enforcement at Spotify scale

Own and lead key technical initiatives across detection, classification, and policy evaluation systems

Develop and maintain ML models for content moderation, including multimodal and LLM-based systems

Build robust evaluation frameworks, including standardized datasets, offline and online metrics, and continuous improvement loops

Drive experimentation to improve model performance, reliability, and fairness in safety-critical systems

Collaborate closely with cross-functional partners in Trust & Safety, Legal, and Public Affairs to align on policy and enforcement needs

Provide technical leadership within the team, mentoring engineers and contributing to ML strategy and prioritization

Represent technical decisions and trade-offs in stakeholder discussions and influence product direction

Who You Are

You have solid experience building and deploying machine learning systems in production environments at scale

You are experienced with training, evaluating, and maintaining ML models using modern frameworks such as PyTorch

You have a deep understanding of machine learning evaluation, including dataset design, metrics, and continuous improvement systems

You know how to design systems that balance performance, reliability, and real-world impact in high-stakes domains

You care about building safe, responsible, and user-centric ML systems

You are comfortable working across disciplines, partnering with legal, policy, and product stakeholders

You have experience leading technical projects and influencing direction within a team or product area

You have experience with distributed systems or backend technologies (e.g., Scala)

Where Youll 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.