ElevenLabs — the AI voice company valued at $11 billion, backed by Andreessen Horowitz, Sequoia, and ICONIQ Growth with $781M raised — is hiring an experienced AI Safety Engineer focused on free-tier abuse detection and prevention, executable from anywhere globally including the UAE. This is a full-time remote product ownership role requiring 6+ years of production backend engineering experience with direct experience in free-tier abuse, fraud detection, or bot farming at scale. You will design and build scalable safety infrastructure — APIs, data pipelines, ML model deployment, and observability systems — protecting ElevenLabs’ platform across its multimodal AI suite used by millions of users worldwide.
About ElevenLabs — Defining the Future of AI Voice at $11B
Founded: January 2023 — launched with the world’s first human-like AI voice model
Valuation: $11B — $781M raised from a16z, Sequoia, ICONIQ Growth, and world-class investors
Platforms: ElevenAgents (voice/chat agents) · ElevenCreative (speech, music, image, video) · ElevenAPI (AI audio foundational models)
Scale: Millions of users · thousands of businesses including Deutsche Telekom and Meta
Team: Researchers, engineers, operators — IOI medalists and ex-founders. No job titles, just impact.
What ElevenLabs Offers — Culture, Growth & Perks
Remote: Fully remote and global — talent over location; offices in London, NY, SF, Warsaw
Learning: Annual discretionary L&D stipend — professional development supported
Travel: Annual social travel stipend — meet colleagues wherever you choose
Offsite: Annual company offsite — past locations include Croatia and Italy
Co-working: Monthly co-working stipend if not near a main hub — flexibility built in
Position Overview
The AI Safety Engineer (Free Tier Abuse) at ElevenLabs is a technically senior, high-ownership product role at one of the most valuable and fastest-growing AI companies in the world. You will be responsible for driving the deployment and operationalisation of automated moderation and guardrail systems that protect ElevenLabs’ platform and millions of users across its multimodal AI space — with a particular focus on detecting and preventing abuse of ElevenLabs’ free tier by bad actors including bot farms, fake accounts, and fraudulent usage patterns.
This is explicitly a product ownership role with full end-to-end technical execution responsibility — from architecture decisions and backend infrastructure design through ML model deployment, API development, data pipeline engineering, observability system implementation, and ongoing safety roadmap contribution. You will bridge the gap between ML research and production-grade safety systems, partnering with ML engineers to translate research models into reliable, scalable, monitored production infrastructure. At a company growing at the speed ElevenLabs is growing, the quality of the safety infrastructure you build determines whether the platform can continue to scale without being undermined by abuse.
Why This Role Matters: ElevenLabs serves millions of users including fast-growing startups and enterprise clients like Deutsche Telekom and Meta — and free-tier abuse at this scale is not a minor nuisance. Bot farms, fake account creation, and systematic free-tier exploitation directly threaten the quality, cost structure, and commercial viability of AI platforms that depend on genuine user growth to justify their valuations and continue their mission. As the AI Safety Engineer, the abuse detection and guardrail systems you build are the technical defences that protect an $11B company’s platform integrity — and every detection signal you engineer, every pipeline you build, and every SLO you establish contributes directly to ElevenLabs’ ability to serve its genuine users without being exploited by bad actors at scale.
What You’ll Do — Key Responsibilities
Scalable Abuse Detection Infrastructure
- Design and build scalable backend infrastructure for abuse detection — deploying AI and ML models into production safety systems that can reliably identify and act on free-tier abuse, bot farming, fake account creation, and other fraudulent usage patterns across ElevenLabs’ multimodal platform at millions-of-user scale
- Architect robust APIs, data pipelines, and service architectures supporting real-time and batch moderation workflows — engineering the complete technical layer that ingests signals, processes them through detection models, and triggers appropriate automated responses with the speed and reliability that real-time platform safety demands
- Partner with ML engineers to translate research abuse detection models into production-ready systems — managing the full journey from research model to deployed, monitored, continuously improving production service, including feature engineering, model serving infrastructure, and integration across ElevenLabs’ product suite
- Drive technical decisions and contribute vision to the safety roadmap — helping define how the next generation of platform guardrails should be built for scale and precision, bringing production engineering perspective to architecture decisions that will shape ElevenLabs’ safety infrastructure for years to come
Observability, SLOs & Production Excellence
- Implement comprehensive monitoring, alerting, and observability systems for all safety infrastructure — building the dashboards, alerting pipelines, and diagnostic tooling that give the safety and engineering teams real-time visibility of abuse detection performance, system health, and emerging threat patterns
- Establish SLIs, SLOs, and performance benchmarks for safety systems — defining the measurable standards that determine whether abuse detection infrastructure is performing at the quality level ElevenLabs’ platform requires, and creating the accountability framework that drives continuous improvement
- Apply an observability-first engineering mindset — ensuring every safety system is built to be deeply observable from day one, with structured logging, distributed tracing, and metric collection that makes system behaviour transparent and debuggable under production conditions and abuse stress scenarios
Backend Engineering & Cloud Infrastructure
- Apply strong production backend experience across distributed systems, APIs, and data pipelines using Python — specifically asynchronous Python and modern backend frameworks — to build safety infrastructure that is reliable, maintainable, and performant under the high-throughput conditions of real-time platform moderation at scale
- Leverage cloud platform expertise (AWS/GCP), containerisation (Docker/Kubernetes), and CI/CD pipeline experience — implementing the full DevOps infrastructure that allows safety systems to be deployed reliably, updated continuously, and scaled elastically in response to evolving abuse patterns and platform growth
- Implement and maintain real-time streaming and event-driven architectures using tools such as Kafka and Redis — engineering the data infrastructure that enables sub-second abuse signal processing, real-time scoring, and immediate automated response to detected free-tier abuse events at platform scale
Requirements
Core Requirements
- Direct experience tackling free-tier abuse, fraud detection, or fake-account/bot farming at scale — this is the most critical requirement; candidates without this specific background will not be considered regardless of general backend engineering experience level
- 6+ years of backend software engineering experience building production systems at scale — with a clear track record of taking systems from 0→1 with measurable impact, including deploying or working alongside ML and AI systems in production environments
- Strong production backend expertise: distributed systems, APIs, data pipelines, and Python proficiency — specifically including asynchronous Python and modern backend frameworks used in high-throughput production service environments
- Infrastructure and DevOps proficiency: cloud platforms (AWS/GCP), containerisation (Docker/Kubernetes), CI/CD pipelines — able to own the full infrastructure lifecycle of production safety systems from initial deployment through ongoing maintenance and scaling
- Observability experience: Prometheus, Grafana, and similar monitoring tools — with a demonstrated mindset of building observable, well-instrumented systems that provide actionable insight into production system behaviour
Bonus Skills
- Experience in Trust & Safety, Content Moderation, or Integrity engineering — with direct domain expertise in the specific challenges of platform safety, policy enforcement, and the adversarial dynamics of abuse at consumer AI platform scale
- MLOps experience — deployment, monitoring, and versioning of ML models in production environments — supporting seamless integration of research abuse detection models into reliable, maintainable production infrastructure
- Experience with SQL, data analysis tools, real-time streaming systems (Kafka, Redis), or event-driven architectures — directly applicable to the data infrastructure requirements of real-time abuse detection and moderation workflows
Why AI Safety Engineer Roles at ElevenLabs Are Exceptional in 2026
ElevenLabs is not a typical AI startup. In less than three years, it has grown from launch to an $11 billion valuation backed by the world’s most respected AI investors — Andreessen Horowitz, Sequoia, and ICONIQ Growth — serving millions of users and enterprise clients including Deutsche Telekom and Meta. The technical challenges at this scale, in a multimodal AI platform that is expanding rapidly across voice, creative, and API products, are genuinely exceptional engineering problems that most backend engineers never encounter.
For an engineer with the specific combination of free-tier abuse experience, production Python backend depth, cloud infrastructure expertise, ML deployment capability, and observability engineering skill this role demands, ElevenLabs offers an extraordinary professional opportunity: a fully remote position at an $11B AI company that values impact over job titles, supports your professional development, funds global team travel, and is building the infrastructure that will define how AI voice and multimodal platforms remain safe and sustainable at the scale of tens of millions of users. That is both technically extraordinary and professionally career-defining in 2026.
Who Should Apply?
- Trust & Safety / Integrity Engineers with Abuse Detection Experience: With 6+ years of production backend engineering and direct experience tackling free-tier abuse, bot farming, or fake account creation at consumer AI or tech platform scale — the primary profile this role is built for
- Fraud Detection Engineers from FinTech or Large-Scale Platforms: With ML-powered fraud detection or anomaly detection production system experience — able to apply that domain expertise to AI platform abuse detection in a high-growth, high-velocity engineering environment
- Senior Backend Engineers with MLOps Capability: With Python production engineering depth, cloud infrastructure proficiency (AWS/GCP/K8s), and experience deploying and monitoring ML models in production — ready to own the full technical lifecycle of AI safety infrastructure
- Observability-First Platform Engineers: With demonstrated Prometheus, Grafana, and distributed systems observability experience — who build monitoring and alerting as a first-class engineering concern and can establish the SLO frameworks that measure safety system performance rigorously
- UAE-Based or Remote-First Senior Engineers: Looking for a full-time remote role at one of the most valuable and fastest-growing AI companies in the world — with the intellectual challenge, mission significance, global team culture, and career trajectory that ElevenLabs’ $11B platform uniquely offers
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