Research Engineer, Data Infrastructure (Language Modeling)
Cartesia · *HQ - San Francisco, CA
About this role
**Research Engineer, Data Infrastructure (Language Modeling)** **About the Role** Build the datasets and systems that power pretraining at Cartesia. You'll write performant, scalable infrastructure to acquire, process, and curate massive datasets while partnering closely with research to optimize data mixtures. **Your Impact** • 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 (ingestion, preprocessing, filtering, deduplication, augmentation) • Design and run ablation experiments to understand how data sources, processing choices, and mixture weights affect model quality • Partner with research and infrastructure teams to co-design data loading, versioning, and experimentation pipelines • Establish rigorous standards for data quality with tight feedback loops between dataset characteristics and model behavior • Identify and source novel datasets; manage relationships and budgets with external data vendors **What You Bring** • Hands-on experience with ML data infrastructure (training pipelines, dataset versioning, large-scale data loading) • Strong modern engineering execution (clean, tested code, fluency with current tools) • Familiarity with building and evaluating datasets for generative models and knowledge of how they're trained/inference **Nice-to-Haves** • Experience with large-scale data processing (Ray, Spark, Kubernetes) • Experience pretraining language models **Details** 🏢 **In-Office:** San Francisco, London, or Bangalore 🌎 **Visa Sponsorship:** Available (case-by-case basis) 💰 **Competitive base salary + equity** 🩺 **Full health coverage** (medical, dental, vision) 🧑🧑🧒🧒 **Parental leave** (9 weeks paternity, 12 weeks maternity) 🏖️ **Flexible PTO** 🍲 **Daily meals & snacks** Cartesia is an equal opportunity employer.
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