Designs, builds, and deploys production ML systems for content safety, moderation, and policy enforcement at Spotify scale. Leads technical initiatives, develops multimodal/LLM models, and collaborates with Trust & Safety, Legal teams on safety-critical systems.
Salary not listed
HybridML Engineering
About the role
What You'll 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
Solid experience building and deploying machine learning systems in production environments at scale
Experienced with training, evaluating, and maintaining ML models using modern frameworks such as PyTorch
Deep understanding of machine learning evaluation, including dataset design, metrics, and continuous improvement systems
Know how to design systems that balance performance, reliability, and real-world impact in high-stakes domains
Care about building safe, responsible, and user-centric ML systems
Comfortable working across disciplines, partnering with legal, policy, and product stakeholders
Experience leading technical projects and influencing direction within a team or product area
Experience with distributed systems or backend technologies (e.g., Scala)
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