Cartesia
Cartesia is hiring: Researcher: Audio (Data) in San Francisco
Cartesia, San Francisco, CA, United States, 94175
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.
The Role
•Lead the design and creation of high-quality datasets tailored for training cutting-edge audio models, focusing on tasks such as speech recognition, enhancement, separation, synthesis, and speech-to-speech systems.
•Develop strategies for curating, augmenting, and labeling audio datasets to address challenges like noise, variability, and diverse use cases.
•Design innovative data augmentation and synthetic data generation techniques to enrich training datasets and improve model robustness.
•Create datasets specifically for speech-to-speech systems, focusing on alignment, phonetic variability, and cross-linguistic considerations.
•Collaborate closely with researchers and engineers to understand model requirements and ensure datasets are optimized for specific architecture and task needs.
•Build tools and pipelines for scalable data processing, labeling, and validation to support both research and production workflows.
What We're Looking For
•Deep expertise in audio data processing, with a strong understanding of the challenges involved in creating datasets for tasks like ASR, TTS, or speech-to-speech modeling.
•Experience with audio processing libraries and tools, such as librosa, torchaudio, or custom pipelines for large-scale audio data handling.
•Familiarity with data augmentation techniques for audio, including time-stretching, pitch-shifting, noise addition, and domain-specific methods.
•Strong understanding of dataset quality metrics and techniques to ensure data sufficiency, coverage, and relevance to target tasks.
•Programming skills in Python and experience with frameworks like PyTorch or TensorFlow for integrating data pipelines with model training workflows.
•Comfortable with large-scale data processing, distributed file systems for audio data storage and processing.
•A collaborative mindset, with the ability to work closely with researchers and engineers to align data design with model objectives.
Nice-to-Haves
•Experience in creating synthetic datasets using generative models or simulation frameworks.
•Background in multimodal data curation, integrating audio with text, video, or other modalities.
•Early-stage startup experience or experience building datasets for cutting-edge research.
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.
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.
The Role
•Lead the design and creation of high-quality datasets tailored for training cutting-edge audio models, focusing on tasks such as speech recognition, enhancement, separation, synthesis, and speech-to-speech systems.
•Develop strategies for curating, augmenting, and labeling audio datasets to address challenges like noise, variability, and diverse use cases.
•Design innovative data augmentation and synthetic data generation techniques to enrich training datasets and improve model robustness.
•Create datasets specifically for speech-to-speech systems, focusing on alignment, phonetic variability, and cross-linguistic considerations.
•Collaborate closely with researchers and engineers to understand model requirements and ensure datasets are optimized for specific architecture and task needs.
•Build tools and pipelines for scalable data processing, labeling, and validation to support both research and production workflows.
What We're Looking For
•Deep expertise in audio data processing, with a strong understanding of the challenges involved in creating datasets for tasks like ASR, TTS, or speech-to-speech modeling.
•Experience with audio processing libraries and tools, such as librosa, torchaudio, or custom pipelines for large-scale audio data handling.
•Familiarity with data augmentation techniques for audio, including time-stretching, pitch-shifting, noise addition, and domain-specific methods.
•Strong understanding of dataset quality metrics and techniques to ensure data sufficiency, coverage, and relevance to target tasks.
•Programming skills in Python and experience with frameworks like PyTorch or TensorFlow for integrating data pipelines with model training workflows.
•Comfortable with large-scale data processing, distributed file systems for audio data storage and processing.
•A collaborative mindset, with the ability to work closely with researchers and engineers to align data design with model objectives.
Nice-to-Haves
•Experience in creating synthetic datasets using generative models or simulation frameworks.
•Background in multimodal data curation, integrating audio with text, video, or other modalities.
•Early-stage startup experience or experience building datasets for cutting-edge research.
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.