Software Engineer, Data Infrastructure

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, 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 (batching, GPU-aware loading, 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. WHAT YOU BRING - 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. - 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 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. MORE DETAILS 🏢 In-office policy: We’re an in-person team based out of offices in 🇺🇸 San Francisco, 🇬🇧 London and 🇮🇳 Bangalore. We love being in the office, hanging out together, and learning from each other every day. 🌎 Visa sponsorship: We provide visa sponsorship support and assess each circumstance on a case-by-case basis. However, visa sponsorship is dependent on many factors, including the role you are applying for, and the location you are going to be based, and so we can't always guarantee success. Your Recruiter will work with you to understand your visa sponsorship needs from the first call. 🚢 We ship fast. All of our work is novel and cutting edge, and execution speed is paramount. We have a high bar, and we don’...