About the role
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. 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. 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
