You will:- Run data mixture ablations and fit scaling laws
- Research efficient data curriculum for various Foundation modeling tasks
- Research contribution and efficiency of different modalities in a multi-modal Foundation Model
- Explore and apply training data attribution (TDA) and data valuation methods to quantify dataset utility and guide data curation for foundation models
You have:- Currently pursuing a PhD in Computer Science, Robotics, Machine Learning, or a related quantitative field
- In-depth knowledge of state of the art foundation models.
- Experience with data pipelines at scale
- Strong foundation in machine learning, deep learning, and statistical modeling/optimization
- Proficiency in Python and modern deep learning frameworks (e.g., JAX, PyTorch) for training and evaluating large-scale neural networks
We prefer:- Experience with Grain
- Research or practical experience with neural scaling laws, data mixture optimization, curriculum learning, or training data attribution
Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in.
The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company's generous benefits programs, subject to eligibility requirements.
Hourly PhD Pay
$85-$85 USD
