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Safety Engineer – Free Tier Abuse

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Remote from
UK, USA, Poland+2 more, Ireland, Japan
Salary
Undisclosed
Department
Cybersecurity
Employment
Full Time
Experience
Open level
Published
Apply before
7 Nov 2026
Listing views
137
Application actions
7
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AI Summary

The role, at a glance.

ElevenLabs is seeking a senior Safety Engineer to build and operate scalable systems that detect and prevent free-tier abuse, fraud, and bot farming. The role owns end-to-end safety infrastructure, including APIs, data pipelines, ML model deployment, real-time and batch moderation workflows, and observability. It requires deep backend engineering expertise, particularly in Python, distributed systems, cloud infrastructure, containers, CI/CD, and production monitoring. The engineer will collaborate closely with ML and full-stack teams while shaping the technical roadmap for platform guardrails. This is a high-autonomy role in a fast-moving global AI company with a strong emphasis on measurable product impact.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a senior, technically broad role requiring 6+ years of production backend experience plus direct exposure to abuse, fraud, or integrity problems at scale. The successful candidate must translate ML capabilities into reliable, observable, low-latency safety systems while making architecture and roadmap decisions independently.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianMarket rate
$180,000
US market range$150k–$220k
AI insightNo actual salary is disclosed in the posting, so these figures are estimates for the US market in USD. For a senior backend-oriented AI Safety/Trust & Safety Engineer with distributed systems, MLOps, cloud, and fraud-abuse prevention experience, a reasonable estimated annual base-salary market range is $150,000-$220,000, with a midpoint of $180,000; equity and other compensation may be additional.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design a system to detect and mitigate free-tier account abuse at scale?

I would begin by defining abuse types and measurable outcomes, then combine account, device, network, behavioral, and usage signals into a risk-scoring pipeline. The system should support both real-time decisions, such as rate limits or challenges, and batch analysis for model retraining and investigator review. I would include feedback loops, feature monitoring, appeal or false-positive analysis, and staged enforcement policies to balance safety with legitimate user experience.

Describe how you have taken an ML model from research into a production backend workflow.

I would explain the model contract, feature availability, latency and reliability requirements, deployment approach, and rollback plan. In production, I prioritize versioned models and features, shadow or canary testing, monitoring for performance and drift, and clear ownership between ML and platform teams. The goal is to make the model an observable, dependable component of a broader service rather than an isolated research artifact.

What SLIs and SLOs would you define for a real-time moderation or abuse-detection service?

Core SLIs would include request latency, availability, error rate, queue lag, throughput, model inference success rate, and decision coverage. Quality-oriented indicators should include precision, recall, false-positive rate, false-negative rate, and enforcement reversal rate where ground truth is available. SLOs should reflect the risk tier of the workflow, with tighter latency and availability objectives for blocking decisions and explicit fallback behavior when dependencies fail.

How would you investigate a sudden increase in fake-account creation without unnecessarily blocking legitimate users?

I would first validate the signal through dashboards segmented by geography, referral source, IP or ASN, device fingerprint, signup flow, and account behavior. I would compare the cohort with historical baselines, identify concentrated attack patterns, and introduce targeted mitigations such as velocity limits, reputation checks, adaptive challenges, or temporary feature restrictions. Each mitigation would be measured through controlled rollout, with false-positive monitoring and a path to quickly reverse overly broad rules.

How do you make architecture decisions in an ambiguous, fast-moving environment?

I start by clarifying the user and risk problem, constraints, expected scale, and success metrics, then propose the simplest design that can meet them. I document meaningful trade-offs around latency, cost, operational burden, and future extensibility, seek input from relevant partners, and make a timely decision with an incremental rollout plan. After launch, I use production data and operational feedback to refine the architecture rather than treating the initial design as final.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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.

About the role

We’re looking for an experienced AI Safety Engineer to drive the deployment and operationalization of automated moderation and guardrail systems that protect our platform and users across a multimodal space, with a particular focus on detecting and preventing abuse of ElevenLabs’ free tiers. You’ll work alongside a team of ML and full stack engineers to build production-grade safety infrastructure from the ground up.

This is a product ownership role where you’ll be responsible for end-to-end technical execution of our safety systems, from architecture to deployment and monitoring. You’ll bridge the gap between ML research and production-grade systems, ensuring our safety infrastructure is robust, observable, and scalable.

What you’ll do:

  • Design and build scalable backend infrastructure for abuse detection, deploying AI/ML models into production systems

  • Architect robust APIs, data pipelines, and service architectures supporting real-time and batch moderation workflows

  • Implement comprehensive monitoring, alerting, and observability systems; establish SLIs, SLOs, and performance benchmarks

  • Partner with ML engineers to translate research models into production-ready systems and integrate them across our product suite

  • Drive technical decisions and contribute vision to the safety roadmap on how the next generation of platform guardrails should be built for scale and precision.

Requirements

  • Direct experience tackling free-tier abuse, fraud detection, or fake-account / bot farming at scale

  • 6+ years of backend software engineering experience building production systems at scale

  • Strong production backend experience: distributed systems, APIs, data pipelines, and Python expertise (asynchronous Python, backend frameworks)

  • Infrastructure & DevOps proficiency: cloud platforms (AWS/GCP), containerization (Docker/K8s), CI/CD pipelines

  • Observability mindset with experience in monitoring tools (Prometheus, Grafana) and building observable systems

  • Track record of taking products or systems from 0→1 with measurable impact, including deploying or working alongside ML/AI systems in production

Bonus:

  • Trust & Safety, Content Moderation, or Integrity engineering experience

  • MLOps experience: deployment, monitoring, and versioning of ML models

  • Experience with SQL, data analysis tools, real-time streaming systems (Kafka, Redis), or event-driven architectures

  • Familiarity with React or modern frontend frameworks

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.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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