Research Engineer, Data Infrastructure (Language Modeling)

Job summary

Responsibilities

Data is the lifeblood of our models, and we are looking for a Research Engineer, Data Infrastructure to build the datasets and systems that power pretraining at Cartesia. In this role, you will write performant, scalable infrastructure to acquire, process, and curate massive datasets, and partner…

• Build and operate performant, scalable data processing infrastructure for acquiring, ingesting, and combining massive text datasets.
• Design and operate scalable, high-throughput, and reproducible data pipelines — covering ingestion, preprocessing, filtering, deduplication, and augmentation.
• Design and run ablation experiments to understand how data sources, processing choices, and mixture weights affect model quality.
• Partner closely with research and infrastructure teams to co-design data loading, versioning, and experimentation pipelines.
• Establish and enforce rigorous standards for data quality, with a tight feedback loop between dataset characteristics and model behavior.

What they're looking for

• Hands-on experience with ML data infrastructure: training data pipelines, dataset versioning, large-scale data loading, and the interplay between data systems and model training…
• Strong modern engineering execution: clean, well-tested code, fluency with current tools, and a willingness to pick the right tool for the problem rather than defaulting to…
• Familiarity with building and evaluating datasets for generative models and reasonable working knowledge of how they're trained and inference.

Tech stack

• Databricks
• Ray
• Spark
• Kubernetes