Cartesia
Tech / AI / Software
SoftwareEngineer,DataInfrastructure
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Software Engineer, Data Infrastructure at Cartesia. Skills: ML data infrastructure, training data pipelines, dataset versioning, large-scale data loading, data systems, model training, inference, multimodal data, audio, text, video, generative models. Contribute to 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 build scalable, high-throughput data pipelines for text, audio, ”
What You'll Achieve.
shape the capabilities and quality of our foundation models
Industry & Context.
pick the right tool for the problem rather than defaulting to familiar patterns
execution speed is paramount
What They're Looking For.
Must Have
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 and inference., Working knowledge of multimodal data, i. e. audio: formats, preprocessing, augmentation, and large-scale storage and streaming patterns., 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 familiar patterns., Track record of driving significant technical projects end-to-end in a fast-moving, research-driven environment., Familiarity with building and evaluating datasets for generative models and reasonable working knowledge of how they're trained and inference.
What You'll Do.
Contribute to Cartesia's multi-modal data strategy across pre-training and post-training
and web-scale sources
with particular depth in audio.
Design and build scalable
high-throughput data pipelines for text
and video — covering ingestion
and data loading for training.
Partner closely with research and inference teams so data systems are co-designed with training and serving infrastructure (batching
evaluation pipelines).
Drive rigorous standards for data quality
with a tight feedback loop between dataset characteristics and model behavior.
Identify and integrate novel datasets
including working with external data vendors and partners.
How You'll Work.
Team & Collaboration
partners closely with research and inference teams; partners closely with research and inference teams so data systems are co-designed with training and serving infrastructure; support each other; open & inclusive culture
Process & Methodology
Track record of driving significant technical projects end-to-end
Full Job Description
ABOUT CARTESIA Our mission is to architect AI that learns from and interacts with the world like humans do. We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models or SSMs, a new primitive for training efficient, large-scale foundation models. Our team combines deep expertise in model innovation and systems engineering paired with a design-minded product engineering team to build and ship cutting edge models and experiences. We're funded by leading investors at Index Ventures and Lightspeed Venture Partners, along with Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks and others. We're fortunate to have the support of many amazing advisors, and 90+ angels across many industries, including the world's foremost experts in AI. ABOUT THE ROLE Data is the lifeblood of our models, and we're looking for a Software Engineer to help build the training data and ML data infrastructure at Cartesia. This role sits at the intersection of data systems, model training, and inference — it is not a siloed data org. You'll design and ship the pipelines, datasets, and infrastructure that feed our pre-training and post-training, with particular depth in audio and other multimodal data. Your work will directly shape the capabilities and quality of our foundation models. This is a hands-on technical role. We're looking for someone fluent at the application and ML infrastructure layer, who ships modern, well-tested code and partners closely with research and inference teams. This is not a traditional data warehousing, analytics, or BI engineering role. YOUR IMPACT - Contribute to 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 build scalable, high-throughput data pipelines for text, audio, and video — covering ingestion, preprocessing, augmentatio
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