elevenlabs - AI Safety Engineer (Data)
Requirements
• Experience as a Data Scientist, or similar, working with real-world datasets and ML models. • Strong grasp of what makes a good dataset: collection, cleaning, sampling, labelling, quality control, evaluation. • Comfortable with Python and SQL for analysis and practical data workflows. • Strong critical thinking and judgement - comfortable with ambiguity and nuance, and able to turn complex guidelines into consistent, scalable decisions. • Autonomous and a clear communicator, able to work across disciplines (policy, engineering, research) in a fast-moving, still-forming environment. • Experience in AI safety, trust & safety, content moderation, or fraud/abuse detection. • Experience building datasets or evaluation frameworks, or with adversarial/safety-focused ML. • Experience managing human-in-the-loop labelling processes, dataset versioning, or ML pipeline tooling. • Experience operationalising guidelines with policy or legal teams. • This role is based in London, where you'll work closely with our AI Safety and Policy teams.
Responsibilities
• Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend. • Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. • Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. • Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend. • Own safety datasets end-to-end: collection, cleaning, labelling, quality control, versioning, and readiness for training and evaluation. • Translate safety policy into clear, consistent labelling and evaluation criteria, working closely with policy specialists. • Design and manage labelling processes, including sourcing, onboarding, and overseeing external contributors to a high quality bar. • Build evaluation workflows for models in production, using real-world data to track performance and surface issues. • Develop lightweight Python/SQL pipelines and tooling to make data work faster and reproducible, partnering with ML engineers on what "training-ready" looks like.
Benefits
• What's the most impactful thing you've built? What was your specific contribution? • How did you know it worked? What did success actually look like? • Have you used ElevenLabs - even in a personal or side project? What did you build or explore?
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