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Research Engineer – Web Crawlers

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Remote from
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Salary
Undisclosed
Employment
Full Time
Experience
Open level
Published
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21 Nov 2026
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34
Application actions
2
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AI Summary

The role, at a glance.

ElevenLabs is seeking a research engineer to own large-scale web crawling systems that collect and prepare high-quality training data for frontier AI models. The role involves building distributed crawlers, designing extraction and deduplication workflows, and managing freshness, recrawl, politeness, and rate-limit strategies across billions of pages. The engineer will also develop targeted pipelines for multilingual, audio, and video content, plus researcher-facing tools for requesting and exploring crawled datasets. Success requires strong distributed-systems engineering, practical crawling expertise, and independent evaluation of data quality, coverage, and compliance. The globally remote position operates in a high-velocity, autonomous environment.

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 highly complex systems role involving reliable web-scale crawling, data quality, and infrastructure processing billions of documents. It requires deep technical judgment across distributed systems, web protocols, data extraction, and ML-data requirements.

Salary analysis

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

Estimated job median (per year)Market rate
$210,000
US market range (per year)$170k–$270k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The annual USD figures are estimated market compensation for a senior research engineer specializing in large-scale distributed crawling and AI training-data infrastructure in the US market; the yearly cadence is inferred from the full-time professional role.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design a crawler capable of processing billions of pages while remaining reliable and polite to target websites?

I would use a distributed frontier backed by durable queues, partition work by host or domain, and enforce per-host rate limits and concurrency controls. Workers would be stateless where possible, with idempotent fetch and processing stages, retries with exponential backoff, and clear observability for throughput, errors, and crawl lag. I would also persist crawl metadata to support resumability, prioritization, and recrawl scheduling.

What techniques would you use to deduplicate web content at large scale?

I would combine exact hashing for byte-identical documents with normalized-text hashes for near-identical pages. For semantic or fuzzy duplicate detection, I would use methods such as shingling and MinHash/LSH, applying them selectively to control cost. The design would retain provenance and duplicate-cluster information so downstream users can choose canonical documents and audit decisions.

How would you evaluate the quality and coverage of a newly crawled dataset?

I would define measurable quality dimensions such as extraction completeness, language accuracy, duplicate rate, content-type distribution, freshness, spam prevalence, and source diversity. Automated sampling and dashboards would highlight regressions, while stratified manual review would validate classifiers and extraction logic. Coverage would be measured against targeted domains, languages, content formats, and expected source inventories.

Describe how you would handle dynamic websites and difficult HTML extraction cases.

I would first prioritize efficient HTTP-based retrieval and robust boilerplate removal because browser rendering is expensive. For sites that require JavaScript, I would use a tightly controlled rendering tier with resource limits, caching, and clear eligibility rules. Extraction would use layered heuristics and structured-data parsing, with confidence signals and raw snapshots retained for debugging and future parser improvements.

How would you build tooling that enables researchers to request and explore crawled data independently?

I would provide a declarative request interface for source, language, media type, freshness, and quality constraints, backed by tracked jobs and reproducible dataset versions. A searchable catalog would expose schema, provenance, crawl dates, quality metrics, and sampling previews. I would also include access controls, audit logs, and monitoring so researchers can understand delivery status and safely use the resulting datasets.

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 are looking for a Research Engineer to join the research team at ElevenLabs, focused on large-scale web crawling for our frontier AI models. The quality of our models is bounded by the quality and scale of the data behind them, and you will own the crawling systems that source world-class data from the open web. You will thrive in this role if you enjoy:

  • Building and operating large-scale, distributed web crawlers that discover, fetch, and extract data across billions of pages reliably and efficiently.

  • Solving hard crawling problems such as content extraction from messy HTML, deduplication at web scale, freshness and recrawl strategies, and politeness and rate-limit handling.

  • Designing targeted crawling pipelines that find high-value data sources, including audio, video, and multilingual content, and turn them into clean training-ready datasets.

  • Creating tooling and infrastructure that lets researchers request, monitor, and explore newly crawled web data quickly and reliably.

Requirements

We do not require any formal certifications or degrees. Instead, we are seeking enthusiastic engineers who can showcase solving impressively hard problems with artifacts such as past projects, designs, or GitHub contributions. Ideally, you bring:

  • Hands-on experience building and scaling web crawlers or scraping systems, ideally in support of machine learning training data.

  • Strong engineering skills in distributed systems at scale (e.g., Kubernetes, queue-based architectures, or custom pipelines processing billions of documents).

  • The capacity to autonomously evaluate the quality, coverage, and compliance of crawled data, and to build the tooling to measure it.

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 >

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