About Cartesia
Our mission is to build the next generation of AI: ubiquitous, interactive intelligence that runs wherever you are. Today, not even the best models can continuously process and reason over a year-long stream of audio, video and text—1B text tokens, 10B audio tokens and 1T video tokens—let alone do this on-device.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.
What You'll Do
- Conduct groundbreaking research in neural network architecture design to advance the state-of-the-art (SOTA) in alternative architectures (e.g., state space models, efficient Transformers, hybrid architectures).
- Design novel architectures that improve model quality, inference efficiency, and adaptability across diverse deployment environments, from cloud to on-device.
- Explore and develop capabilities such as statefulness, long-range memory, and innovative conditioning mechanisms for enhancing model expressiveness and generalization.
- Investigate how architectural decisions impact model trade-offs, including scalability, robustness, latency, and energy efficiency.
- Develop new frameworks and tools to evaluate architectural innovations, benchmarking performance across research and production settings.
- Collaborate with cross-functional teams to translate architectural research into scalable and impactful systems for real-world applications.
What You'll Bring
- Deep expertise in architecture design, with experience in researching or deploying advanced architectures (e.g., state space models, transformers, RNN variants, CNN variants).
- Strong understanding of how architectures interact with system constraints, including deployment in cloud environments or on-device.
- Proficiency in designing architectures that balance quality, efficiency, and adaptability across different use cases and modalities (e.g., vision, audio, text).
- Familiarity with generative modeling paradigms like autoregressive and diffusion models, and designing capabilities such as statefulness and conditioning in deep learning models.
- A proven research track record in top-tier ML/AI venues (e.g., NeurIPS, ICML, ICLR, CVPR) or demonstrable contributions to state-of-the-art architectures.
- Exceptional analytical and problem-solving skills, with a focus on experimentation and iterative refinement.
- Strong programming skills in deep learning frameworks such as PyTorch or TensorFlow, and experience with profiling tools for understanding model performance.
Nice-to-Haves
- Prior research or publications in state space models, efficient Transformers or other alternative architectures.
- Research or practical experience in designing architectures for multi-modal systems.
- Early-stage startup experience or a track record of rapid innovation in R&D environments.
Our culture
🏢 We’re an in-person team based out of San Francisco. We love being in the office, hanging out together and learning from each other everyday.🚢 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’t sacrifice quality and design along the way.🤝 We support each other. We have an open and inclusive culture that’s focused on giving everyone the resources they need to succeed.
Our perks
🍽 Lunch, dinner and snacks at the office.🏥 Fully covered medical, dental, and vision insurance for employees.🏦 401(k).✈️ Relocation and immigration support.🦖 Your own personal Yoshi.
