Research Engineer, Data Infrastructure

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 Cancun. 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. 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.