Research Intern

Cartesia • San Francisco, CA

1d • Onsite

About The Position

As a research intern, you'll have the opportunity to work inside our research team in pioneering multimodal models built on new model architectures.
Your main responsibility will be to push the quality, efficiency and capabilities of our pretrained models, in collaboration with a variety of machine learning, data and systems engineering stakeholders.

Requirements

• Comfortable navigating complex machine learning codebases.
• Deep machine learning background, including a strong grasp of fundamentals in sequence modeling, generative models and common model architecture families (RNNs, CNNs, Transformers).
• Experienced model trainer, ideally previously wrote and pretrained large-scale models.
• Proficient in Python and Pytorch (or similar framework) and tensor programming more broadly.
• Familiarity with efficiency tradeoffs in designing model architectures for accelerators such as GPUs.
• Pursuing advanced degrees in machine learning (MS/PhD). Regardless of background, consider applying if you have strongly relevant experience.

Nice To Haves

• Prior research experience in advancing state space models or implementing them in practice.
• Experience in optimizing model inference with CUDA, Triton or other frameworks.

Responsibilities

• implement new model backbones, architectures and training algorithms
• rapidly run and iterate on experiments and ablations
• build training infrastructure that scales to massive multimodal datasets
• stay up-to-date on new research ideas

What This Job Offers

• Career Level: Intern