Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Full-Stack Engineer – Ads Engine
- Salary
- Undisclosed
- Department
- Software Engineering
- Employment
- Full Time
- Experience
- Open level
- Published
- Apply before
- 5 Dec 2026
- Listing views
- 18
- Application actions
- 0
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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 team offsite: Each year, we bring your entire team together in a new location – past offsites have included Croatia, Italy and Cancún
Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend.
About the role
We build an ads engine that learns as it goes: brand book, taste, and what actually performs in every market. It plans, generates, localizes, launches and learns from ads across the major ad platforms, using ElevenLabs’ full audio and video stack.
You’ll be a founding engineer for a product area of your own. There’s no PM and no handed-down spec. You’ll talk to users, decide what to build, and ship it across the stack, from data pipelines and agent tools to the UI. Early work could include feeding competitive intelligence into the engine’s weekly briefs or launching a new ad format.
We’re looking for founders and founding engineers. You’d rather ship a rough v0 on Friday than a perfect design doc. Strong engineering is the minimum. What sets people apart is product judgment and ownership.
Requirements
Has built and shipped a product end-to-end without anyone handing them a spec, ideally as a founder or one of the first engineers.
Strong full-stack product engineer (frontend- or backend-leaning is fine) who can build a pipeline, an agent tool and the UI
Builds with LLMs and agents pragmatically: tools, retrieval and evals, not model research.
Talks to users without being asked, and can say what they’d build first and why.
Bonus: experience with ad platforms, adtech or marketing tools; analytics and attribution; web builders and CMSs; taste in creative.
Location
This role is remote-first, so it can be executed from anywhere between the US East Coast and India, with the ability to operate in European timezones required. If you prefer, you can work from our offices in Bangalore, Dublin, London, New York 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.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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Sample interview questions
I would explain how I identified the user problem, validated assumptions through direct conversations and data, scoped a minimal first version, and iterated after launch using measurable adoption and outcome metrics.
I would begin with a narrow workflow such as generating a weekly creative brief from brand guidelines, campaign data, and competitor signals. I would define clear inputs, retrieval sources, human review points, evaluation criteria, and a feedback loop before expanding to campaign launch automation.
I use constrained tool interfaces, grounded retrieval, structured outputs, automated evaluations, observability, and fallbacks. I also measure task-specific quality and continuously review real user failures to improve prompts, data, and workflows.
I optimize for a small, testable vertical slice while protecting the critical foundations: data integrity, security, observability, and maintainable interfaces. I document key trade-offs, ship quickly, and schedule follow-up work based on evidence from production.
I would establish a regular cadence of customer interviews, support-ticket review, and usage-data analysis. I would translate recurring pain points into hypotheses, share lightweight prototypes early, and prioritize work based on user impact, strategic value, and delivery effort.
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