Research Engineer - Data Infrastructure

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 ...

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.

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.
 

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.