Multimodal Data Infrastructure Engineer — Equity Options

Cartesia AI, Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 250,000; Full time

Benefits offered by this job

Compensation

Health Insurance

Parental Leave

401(k)

Commuter Allowance

Flexible PTO

Meals & Snacks

Your own personal Yoshi

Job summary

Cartesia is hiring a Software Engineer, Data Infrastructure to own data strategy and pipelines that power our research-grade models. You will build datasets and scalable systems to acquire, process, and curate multimodal data, collaborating with research and inference teams.

You will ensure data quality, manage datasets and vendor relationships, and guide the evolution of training data with a focus on audio and web-scale sources.

Qualifications

• Hands-on experience building ML data pipelines and dataset versioning.
• Experience with multimodal data including audio formats and processing.
• Ability to work with research and inference teams to align data systems.
• Strong coding practices and ability to pick the right tool for the problem.

Responsibilities

• Define Cartesia's multi-modal data strategy across pre-training and post-training, spanning human, synthetic, and web-scale sources, with particular depth in audio.
• Design and operate scalable, high-throughput data pipelines for text, audio, and video — covering ingestion, preprocessing, augmentation, dataset versioning, and data loading for training.
• Partner closely with research and inference teams so data systems are co-designed with training and serving infrastructure.
• Establish and enforce rigorous standards for data quality, with a tight feedback loop between dataset characteristics and model behavior.
• Identify and source novel datasets; manage relationships and budgets with external data vendors and partners.

Skills

ML data infra

Dataset versioning

Multimodal data handling

Data pipelines

Cross-functional collaboration

Job description

Cartesia is hiring a Software Engineer, Data Infrastructure to own data strategy and pipelines that power our research-grade models. You will build datasets and scalable systems to acquire, process, and curate multimodal data, collaborating with research and inference teams.

You will ensure data quality, manage datasets and vendor relationships, and guide the evolution of training data with a focus on audio and web-scale sources.