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Engineering – Internal AI Transformation

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Remote from
UK, USA, Canada+5 more, Germany, Italy, Poland, Sweden, Ireland
Salary
Undisclosed
Employment
Full Time
Experience
Open level
Published
Apply before
22 Nov 2026
Listing views
13
Application actions
1
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AI Summary

The role, at a glance.

This is a senior internal AI engineering role focused on eliminating manual work across go-to-market, operations, and finance functions. The engineer will design, ship, and operate agentic workflows, reusable automation services, and integrations with systems such as Salesforce, Slack, and Ashby. Core technical requirements include Python, SQL, APIs, webhooks, LLM prompting, RAG, cloud infrastructure, Kubernetes, Docker, and enterprise authentication. Success requires independently identifying operational problems, rapidly validating solutions with internal stakeholders, and building reliable, auditable AI systems. The role operates in a lean, high-velocity environment with substantial ownership and cross-functional influence.

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 insightThe role combines production backend engineering, AI-agent design, infrastructure operations, security, and ambiguous business-process discovery. It requires independently delivering reliable systems that affect multiple internal business functions.

Salary analysis

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

Estimated job median (per year)Market rate
$190,000
US market range (per year)$160k–$240k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. Estimated annual USD compensation for a senior internal AI/backend engineer in the US market is approximately $160,000–$240,000, with a midpoint of $190,000; yearly cadence is inferred from the full-time professional role and US market convention.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe an AI automation you took from an unclear business problem to production.

I would begin by mapping the current workflow, identifying measurable failure points and confirming the highest-value intervention with users. I would then build a narrow prototype, test it with representative data and edge cases, add observability and approval controls, and iterate based on operational outcomes before scaling it.

How would you build a reliable agentic workflow for a finance or operations process?

I would separate deterministic workflow steps from LLM-based decisions, use structured inputs and outputs, and define explicit tool permissions. I would add validation, human review for high-impact actions, comprehensive audit logs, retries, monitoring, and evaluation datasets to measure accuracy, latency, and failure modes.

What considerations matter when integrating LLM workflows with systems such as Salesforce and Slack?

I would use secure service identities, least-privilege scopes, secret management, idempotent API design, and webhook signature validation. I would also account for rate limits, data classification, error recovery, user notifications, and auditability so that the automation is safe and maintainable.

How do you evaluate whether an LLM or RAG workflow is ready for production?

I would create a representative evaluation set covering normal requests, ambiguous inputs, adversarial cases, and sensitive-data scenarios. Readiness would require defined quality thresholds, grounded-response checks, tool-call validation, latency and cost targets, and monitoring that detects drift after release.

How do you prioritize automation opportunities across multiple internal teams?

I would rank opportunities by business impact, frequency of the manual task, implementation effort, operational risk, and the availability of reliable data and system access. I would favor small, measurable wins that establish reusable patterns while reserving deeper investment for high-volume or strategically important workflows.

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.

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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