Tech Lead Manager, Data Infrastructure

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.

Data is the lifeblood of our models, and we are looking for a TLM, Data Infrastructure to own the strategy and execution for all data at Cartesia. This is a critical leadership role, where you will be responsible for building and managing the datasets that power our cutting-edge research. You will lead a talented team of data engineers and specialists to acquire, process, and curate massive multimodal datasets. Your vision will directly shape the capabilities and quality of our foundational models.

• 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.

• Lead, mentor, and eventually manage a team of engineers building dataset and ML data infrastructure.

• 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 (batching, GPU-aware loading, evaluation pipelines).

• 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.

• 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 leading and growing a high-impact engineering team 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.

🏢 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.