Cartesia
Researcher, Models
*HQ - San Francisco, CA
Sponsorship not specifiedDetected 587 days ago
DatabricksCloud PlatformsMachine LearningDeep LearningTensorFlowPyTorchNLPA/B TestingSystems EngineeringResearchProblem SolvingAdaptability
About the role
- 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.
Responsibilities
- 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.
- 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.
- 🦖 Your own personal Yoshi
Requirements
- Strong understanding of how architectures interact with system constraints, including deployment in cloud environments or on-device.
- 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.
- 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.
- Deep expertise in architecture design, with experience in researching or deploying advanced architectures (e.g., state space models, transformers, RNN variants, CNN variants).
- 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.
- 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.
- Prior research or publications in state space models, efficient Transformers or other alternative architectures.
- Note: Cartesia participates in E-Verify and will provide the federal government with Form I-9 information to confirm employment eligibility after hire.
- 🏢 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.
Compensation
- 💰 Compensation Competitive base salary alongside attractive equity package.
Benefits
- 💰 Compensation Competitive base salary alongside attractive equity package.
- 🩺 Health Insurance Fully covered medical insurance along with dental and vision for you and your family.
- 🧑🧑🧒🧒 Parental Leave 9 weeks paternity & 12 weeks maternity leave
- 🚆 Commuter Allowance A monthly stipend to help you get to and from the office.
- 🏖️ Flexible PTO Take as much time as you need to recharge your batteries.
- We consider qualified applicants without regard to race, color, religion, sex, national origin, age, disability, veteran status, genetic information, or any other legally protected status.
Equal opportunity
- equal opportunity employer.
Visa & Work Authorization
- 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.
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This listing is sourced directly from Cartesia's careers page and normalized into a canonical job model.