Safety Engineer (Backnd Leaning)
Remote · Dublin · Tokyo · New York · Los Angeles · Poland · London · Boston · San Francisco · Washington, D.C. · 5+ years experience · Full-time · Remote · Posted Oct 14, 2026
At a glance
- Experience
- 5+ years experience
- Employment
- Full-time
- Workplace
- Remote
- Location
- Remote · Dublin · Tokyo · New York · Los Angeles · Poland · London · Boston · San Francisco · Washington, D.C.
About ElevenLabs
- ElevenLabs is an AI research and product company transforming how we interact with technology.
- We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $22B - multiples of 11, always.
- We have expanded from voice into three main platforms:
- - ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale.
- - ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages.
- - ElevenAPI gives developers access to our leading AI audio foundational models.
About the role
- ElevenLabs is seeking a Safety Engineer (Backend Leaning) to design, develop, and scale automated moderation systems, abuse detection capabilities, and guardrails for AI agents.
Responsibilities
- Build safety systems: Design, develop, and scale automated moderation systems, abuse detection capabilities, and guardrails for AI agents across ElevenLabs’ products.
- Respond to emerging threats: Partner closely with the Investigations team to identify emerging abuse patterns, understand evolving threats, and continuously improve existing safety systems, detection mechanisms, and guardrails to address new risks.
- Own production infrastructure: Build and maintain a growing ecosystem of internal safety services, taking ownership of their architecture, deployment, reliability, monitoring, and ongoing operation.
- Bring ML models into production: Partner closely with ML engineers to deploy and integrate fine-tuned and proprietary ML models into production safety systems, ensuring they operate reliably and efficiently at scale.
- Build end-to-end solutions: Develop backend services, APIs, internal tools, and user-facing interfaces that enable effective moderation, safety operations, and policy enforcement.
- Collaborate across teams: Work closely with Product, Legal, and Safety Policy teams to translate business, regulatory, and policy requirements into scalable engineering solutions.
- Product: Understand and navigate the tradeoffs between product growth, user experience, and platform safety.
- Legal: Translate legal and regulatory requirements into practical, scalable safety systems.
- Safety Policy: Collaborate on how safety policies are implemented technically, while helping inform policy decisions through an understanding of engineering capabilities and constraints.
- Shape our technical direction: Contribute to architectural decisions and help define how our safety infrastructure evolves as our products, users, and safety challenges grow.
Requirements
- 5+ years of software engineering experience building and operating production systems.
- Strong backend engineering experience, including designing APIs, working with databases, and building scalable services.
- Proficiency in Python and experience with modern backend frameworks.
- Experience building and maintaining distributed systems and cloud-based applications.
- Comfortable owning systems end to end, from technical design and implementation through deployment, monitoring, and ongoing maintenance.
- Experience working across different parts of the stack, with the ability to build internal tools or frontend interfaces when needed.
- Strong problem-solving skills and an ability to navigate ambiguous requirements and make pragmatic engineering decisions.
- Ability to collaborate effectively with engineers and non-technical stakeholders across different functions.
Preferred qualifications
- Experience building Trust & Safety, content moderation, fraud detection, or abuse prevention systems.
- Experience building guardrails or safety infrastructure for AI agents and LLM-based applications.
- Experience deploying and integrating ML models into production applications.
- Familiarity with Kubernetes, Docker, CI/CD pipelines, and cloud infrastructure (AWS/GCP).
- Experience with monitoring and observability tools such as Prometheus and Grafana.
- Experience with React or other modern frontend frameworks.
- Experience with real-time data processing, event-driven architectures, or technologies such as Kafka and Redis.
Benefits
- Professional development through an annual discretionary stipend
- Annual discretionary stipend to meet up with colleagues
- Annual company offsite
- Monthly co-working stipend for those not located near a main hub
Workplace
- Role is remote and can be executed globally.
Skills
- Python
- Kubernetes
- Docker
- AWS
- GCP
- Prometheus
- Grafana
- React
- Kafka
- Redis
