Researcher, Model Architecture
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.
Your Impact
Conduct groundbreaking research in neural network architecture design to advance the state-of-the-art 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 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 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
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 every day.
🚢 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 or design along the way.
🤝 We support each other. We have an open & inclusive culture that’s focused on giving everyone the resources they need to succeed.