[All remote jobs](https://jobicy.com/jobs.md)[![Elevenlabs logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/e01b4c5d-221.png)](https://jobicy.com/company/elevenlabs.md)[Elevenlabs](https://jobicy.com/company/elevenlabs.md) # Safety Engineer (Backend Leaning) Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/elevenlabs.md)ShareRemote from[UK](https://jobicy.com/job-region/uk.md), [USA](https://jobicy.com/job-region/usa.md), [Poland](https://jobicy.com/job-region/poland.md)+2 more, [Ireland](https://jobicy.com/job-region/ireland.md), [Japan](https://jobicy.com/job-region/japan.md)SalaryUndisclosedDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceOpen levelPublished26 Oct 2026Apply before25 Nov 2026Listing views50Application actions3Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. ElevenLabs is seeking a senior backend-oriented Safety Engineer to build AI safety infrastructure for moderation, abuse detection, policy enforcement, and agent guardrails. The role owns production services end to end, including architecture, APIs, deployment, reliability, monitoring, and ongoing operation. It partners with investigations, ML, product, legal, and safety-policy teams to turn emerging threats and regulatory requirements into scalable technical controls. Candidates need at least five years of production software-engineering experience, strong Python and backend skills, and comfort with distributed cloud systems. Experience in trust and safety, LLM guardrails, ML model integration, Kubernetes, observability, or event-driven systems is advantageous. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a senior, high-impact engineering role requiring ownership of safety-critical distributed systems in a fast-moving AI environment. The engineer must make pragmatic decisions amid ambiguous threats while aligning technical solutions with legal, policy, product, and ML stakeholders. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Highly competitive$210,000US market range (per year)$180k–$240k0$264k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The yearly cadence and USD market estimate are inferred for a senior backend/software safety engineering role with 5+ years of experience in the US market. ## Core skills Skills and capabilities most closely associated with this opportunity. [Python](https://jobicy.com/jobs?search_keywords=Python.md)[Backend Engineering](https://jobicy.com/jobs?search_keywords=Backend%20Engineering.md)[API Design](https://jobicy.com/jobs?search_keywords=API%20Design.md)[Distributed Systems](https://jobicy.com/jobs?search_keywords=Distributed%20Systems.md)[Cloud Infrastructure](https://jobicy.com/jobs?search_keywords=Cloud%20Infrastructure.md)[Trust and Safety](https://jobicy.com/jobs?search_keywords=Trust%20and%20Safety.md)[Content Moderation](https://jobicy.com/jobs?search_keywords=Content%20Moderation.md)[Abuse Detection](https://jobicy.com/jobs?search_keywords=Abuse%20Detection.md)[AI Guardrails](https://jobicy.com/jobs?search_keywords=AI%20Guardrails.md)[Machine Learning Deployment](https://jobicy.com/jobs?search_keywords=Machine%20Learning%20Deployment.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[Docker](https://jobicy.com/jobs?search_keywords=Docker.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md)[Observability](https://jobicy.com/jobs?search_keywords=Observability.md)[Database Design](https://jobicy.com/jobs?search_keywords=Database%20Design.md) Sample interview questionsHow would you design an abuse-detection service for a high-volume AI agent platform?I would begin by defining abuse categories, risk signals, policy actions, and latency requirements with safety and product stakeholders. I would use an event-driven pipeline to collect signals, combine deterministic rules with model-based scoring, store auditable decisions, and expose enforcement APIs. The system would include rate limiting, feedback loops, versioned policies, monitoring for precision and recall, and a safe rollback path for rule or model changes. Describe how you would deploy an ML moderation model into a production backend service. I would package and version the model with clear input and output contracts, then deploy it behind a scalable service or inference endpoint. I would implement timeouts, fallbacks, model and feature monitoring, canary releases, and evaluation against labeled traffic before broader rollout. I would also ensure decisions are logged with model version and relevant metadata to support investigations and future retraining. How do you balance false positives and false negatives in safety systems? I treat the balance as a policy and product decision informed by harm severity, user impact, and operational capacity. For severe risks, I would generally optimize for recall and route uncertain cases to additional checks or human review; for lower-risk cases, I would protect user experience by requiring stronger evidence before enforcement. I would continuously measure outcomes, investigate error clusters, and tune thresholds by risk category. Tell us about your approach to operating a critical distributed service reliably. I define reliability objectives early, instrument core request paths, and build dashboards and alerts around availability, latency, error rates, and dependency health. I use automated deployments with testing and gradual rollout, capacity planning, runbooks, and incident reviews to improve the system over time. I also design for failure through idempotency, retries with backoff, queues, circuit breakers, and graceful degradation. How would you communicate a technical safety limitation to legal and policy stakeholders? I would explain the limitation in plain language, quantify its impact where possible, and distinguish known behavior from uncertainty. I would present feasible mitigation options with tradeoffs in accuracy, latency, cost, implementation time, and user experience. My goal would be to help stakeholders make an informed risk decision while documenting the chosen policy and the engineering plan to implement it. Opportunity details ## About this role. ### 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. Everything we do is the result of the creativity and commitment of our team – builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. ### How we work * High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. * Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. * AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. * Excellence everywhere: Everything we do should match the quality of our AI models. * Global team: We prioritize your talent, not your location. ### What we offer * Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. * Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact – beyond your immediate role and 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. ### What you’ll do * 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. ### Bonus * 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. ### Location This role is remote and can be executed globally. If you prefer, you can work from our offices in London, New York, San Francisco, and Warsaw. #LI-remote We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses. Show more [Apply now >](https://jobicy.com/jobs/157048-safety-engineer-backend-leaning.md) > Annual salary information is not provided for this position. 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