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Engineering – Internal AI Transformation

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Salary
Undisclosed
Employment
Full Time
Experience
Open level
Published
Apply before
7 Nov 2026
Listing views
35
Application actions
2
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AI Summary

The role, at a glance.

ElevenLabs is seeking an Internal AI Engineer to build and deploy agentic AI automations for GTM, Operations, and Finance teams. The role combines backend engineering, workflow orchestration, internal systems integrations, and AI reliability practices. The engineer will independently identify operational pain points, ship production solutions, and establish reusable automation patterns and guardrails. Core technical requirements include Python, SQL, APIs, webhooks, LLM/RAG patterns, cloud infrastructure, Kubernetes, Docker, and enterprise authentication. This is a globally remote, high-velocity role with substantial ownership and cross-functional partnership expectations.

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

5/5
IndependentCollaborative
AI insightThis is a senior-level, ambiguous engineering role requiring production-grade AI workflow design across multiple business systems. Success requires both deep backend and infrastructure capability and the judgment to safely operationalize LLM-based automations with minimal specification.

Salary analysis

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

Estimated job medianMarket rate
$190,000
US market range$155k–$230k
AI insightNo salary was disclosed. Estimated US-market yearly base salary for a senior Internal AI Engineer / forward-deployed automation engineer is $155,000-$230,000 USD, with an estimated midpoint of $190,000; actual compensation may vary by location, level, equity, and bonus structure.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe an internal AI automation you took from an ambiguous business problem to production. How did you measure its impact?

I would begin by mapping the current workflow, identifying failure points and baseline metrics such as handling time, error rate, and throughput. I would ship a narrow pilot with clear human-review boundaries, instrument quality and adoption metrics, then iterate based on operational feedback before expanding the automation.

How would you design a reliable AI agent that updates records across systems such as Salesforce, Slack, and Ashby?

I would use an orchestration layer with typed inputs and outputs, explicit tool permissions, idempotent actions, retries, and durable audit logs. High-impact actions would require validation rules and, where appropriate, human approval; I would also monitor tool failures, model quality, latency, and downstream data consistency.

What approach would you use to evaluate an LLM workflow before and after deployment?

I would create a representative evaluation set from real workflows, define task-specific success criteria, and test correctness, groundedness, safety, latency, and cost. In production, I would track automated quality signals, sampled human review, user feedback, and regression alerts when prompts, models, or integrations change.

How do RAG patterns help with internal automation, and what are their main risks?

RAG can ground agent outputs in current internal policies, process documentation, and account data without relying solely on model memory. The key risks are poor retrieval quality, stale or unauthorized content, prompt injection, and overconfident responses, so I would enforce access controls, provenance, retrieval evaluation, and safe fallbacks.

How would you decide whether to use a no-code platform such as n8n or build a custom service?

I would use n8n for fast, low-risk integrations and workflow experimentation where its connectors and observability meet the operational need. I would build a custom service when the workflow requires complex state management, strong security controls, high scale, rigorous testing, reusable domain logic, or tighter reliability guarantees.

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.

The role / impact
As an Internal AI Engineer at ElevenLabs, you’ll be embedded at the frontier of how we scale – acting as a forward-deployed engineer across our GTM, Operations, and Finance teams. You won’t just build tools; you’ll co-design and ship agentic AI workflows that eliminate manual toil and fundamentally reshape how our people operate.

Your impact goes beyond the code. You’ll own high-stakes automations from discovery to production, acting as a technical partner to internal teams. The patterns, guardrails, and tooling you define will become the blueprint other teams reach for – the standard for how ElevenLabs harnesses AI safely and at speed.

The team / how we work
You’ll join a lean, high-velocity team with a shared obsession: turning broken, manual processes into elegant automated systems. We move fast through rapid experimentation, work closely with operators and business owners to uncover the real problem, and ship pragmatic solutions without waiting for perfect specs.

What you’ll actually be working on

  • Designing and iterating on AI agents and workflow orchestrations using tools like ElevenAgents, Claude, and n8n.

  • Integrating AI systems with our core business stack – Salesforce, Slack, Ashby, and more

  • Building reusable automation services, patterns and shared templates that multiply the output of every team you touch

  • Owning experiments end-to-end: spotting the opportunity, building the solution, and measuring the impact

  • Developing evaluation and monitoring frameworks so our AI-native workflows are reliable, auditable, and safe

    What we’re looking for

  • Proven experience building and shipping automations or applications in production – you know what it takes to go from fuzzy business pain to something real teams depend on

  • AI-First Instincts: Strong familiarity with LLM capabilities, prompting strategies, RAG patterns, and a genuine passion for building agentic workflows.

  • Deep Backend Expertise : Strong Python and SQL and system design patterns (APIs, webhooks, orchestration layers)

  • A systems-thinking mindset : you design with security, auditability, and blast radius in mind from the start

  • Builder and AI-First instincts: you don’t wait to be told what to build – you spot the pattern, propose the fix, and ship it.

  • Infra & DevOps Instincts: Hands-on experience operating cloud infrastructure (GCP preferred), Kubernetes, Docker, and enterprise authentication flows

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. Specific teams may have specific location preferences.

#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 >

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