[All remote jobs](https://jobicy.com/jobs.md)[![Elevenlabs logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/e01b4c5d-221.png)](https://jobicy.com/company/elevenlabs.md)[Elevenlabs](https://jobicy.com/company/elevenlabs.md) # Research Engineer – Data Infrastructure Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/elevenlabs.md)ShareRemote from[UK](https://jobicy.com/job-region/uk.md), [USA](https://jobicy.com/job-region/usa.md), [Poland](https://jobicy.com/job-region/poland.md)+1 more, [Bulgaria](https://jobicy.com/job-region/bulgaria.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceOpen levelPublished24 Oct 2026Apply before23 Nov 2026Listing views35Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. ElevenLabs is seeking a Research Engineer to build the data infrastructure behind frontier AI models. The role owns large-scale pipelines for collecting, processing, filtering, transforming, and curating training data. Responsibilities also include training data-processing models, developing curation strategies, and creating reliable tooling for researchers working with massive datasets. The ideal candidate has strong distributed-systems engineering experience, machine-learning data pipeline knowledge, and the ability to work autonomously in a high-velocity global team. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a highly technical role combining distributed data infrastructure, ML-oriented data curation, and measurable model-quality improvement. The candidate must independently solve difficult systems problems at significant scale in a rapid experimentation environment. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$210,000US market range (per year)$175k–$270k0$297k 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 yearly compensation estimate is an AI-generated USD market estimate for a senior-level research/data infrastructure engineering role in the US market; the annual cadence is inferred from the full-time professional role convention. ## Core skills Skills and capabilities most closely associated with this opportunity. [Distributed data processing](https://jobicy.com/jobs?search_keywords=Distributed%20data%20processing.md)[Machine learning pipelines](https://jobicy.com/jobs?search_keywords=Machine%20learning%20pipelines.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[Data engineering](https://jobicy.com/jobs?search_keywords=Data%20engineering.md)[Data curation](https://jobicy.com/jobs?search_keywords=Data%20curation.md)[Data quality assessment](https://jobicy.com/jobs?search_keywords=Data%20quality%20assessment.md)[Dataset deduplication](https://jobicy.com/jobs?search_keywords=Dataset%20deduplication.md)[Data labeling](https://jobicy.com/jobs?search_keywords=Data%20labeling.md)[Quality scoring](https://jobicy.com/jobs?search_keywords=Quality%20scoring.md)[Data augmentation](https://jobicy.com/jobs?search_keywords=Data%20augmentation.md)[Classifier training](https://jobicy.com/jobs?search_keywords=Classifier%20training.md)[Large-scale data pipelines](https://jobicy.com/jobs?search_keywords=Large-scale%20data%20pipelines.md)[Research tooling](https://jobicy.com/jobs?search_keywords=Research%20tooling.md)[Web crawling](https://jobicy.com/jobs?search_keywords=Web%20crawling.md)[Model evaluation](https://jobicy.com/jobs?search_keywords=Model%20evaluation.md) Sample interview questionsHow would you design a pipeline to collect, process, and curate a large training dataset?I would separate ingestion, validation, transformation, deduplication, quality scoring, labeling, and publishing into observable and reproducible stages. Each stage would use versioned data artifacts, scalable distributed execution, explicit quality metrics, and lineage tracking so that downstream model results can be tied back to data changes. How do you evaluate whether a data curation strategy improves a model? I would establish a controlled baseline dataset and model-training configuration, then run comparable experiments with the proposed curation change. I would assess benchmark performance, error slices, coverage, data-quality metrics, training stability, and cost, while ensuring the observed effect is statistically and operationally meaningful. Describe how you would approach deduplication for web-scale multimodal or text data. I would use layered deduplication: exact hashes for identical content, normalized-content hashes for superficial variants, and approximate similarity methods such as MinHash or embedding-based nearest-neighbor search for semantic duplicates. Thresholds should be calibrated against sampled review sets to avoid removing valuable legitimate variation. What operational practices make distributed data pipelines reliable? I would prioritize idempotent jobs, partition-aware processing, retries with bounded backoff, checkpointing, schema contracts, and clear failure isolation. I would also implement monitoring for throughput, freshness, quality drift, cost, and failed records, with alerts and replay mechanisms for recovery. How would you balance rapid experimentation with data governance and reproducibility? I would provide researchers with self-service, versioned datasets and composable pipeline components while enforcing metadata, lineage, access controls, and documented evaluation criteria. This keeps iteration fast while ensuring experiments can be reproduced and data provenance remains auditable. 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 the data infrastructure that powers 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 systems that make world-class data possible. You will thrive in this role if you enjoy: * Building large-scale data pipelines for collecting, processing, filtering, and transforming datasets used to train state-of-the-art models. * Training models used in our data processing pipelines, such as classifiers, quality filters, and labeling models. * Designing data curation strategies such as deduplication, quality scoring, labeling, and augmentation that measurably improve model performance. * Creating tooling and infrastructure that lets researchers explore and train on massive datasets 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: * Experience building data-intensive systems, ideally in support of machine learning training pipelines. * Strong engineering skills in distributed data processing at scale (e.g., Kubernetes, or custom pipelines over large datasets). * The capacity to autonomously evaluate how data quality, composition, and curation affect model outcomes, and to build the tooling to measure it. Bonus: Experience building or operating web crawlers. ### 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. Show more [Apply now >](https://jobicy.com/jobs/156701-research-engineer-data-infrastructure.md) > Annual salary information is not provided for this position. 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