About the role#
As a Research Intern in the Model Shaping team, you will focus on tailoring open foundation models for downstream applications. You will work on advanced post-training methods including supervised learning, preference optimization, and reinforcement learning. The team also explores new techniques for efficient neural network training, such as distributed training and algorithmic optimization, and develops robust methods for evaluating foundation model capabilities.
What you'll do#
- Research and implement novel techniques in focus areas including model training, optimization, and evaluation.
- Design and conduct rigorous experiments to validate hypotheses.
- Document findings in scientific publications and blog posts.
- Integrate research results directly into Together products.
What you'll need#
- Currently pursuing a Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field.
- Strong knowledge of Machine Learning and Deep Learning fundamentals.
- Hands-on experience with deep learning frameworks such as PyTorch or JAX.
- Familiarity with the Transformer architecture and recent developments in foundation models.
- Prior research experience with training foundation models or efficient machine learning is preferred.
Location & details#
- Location: San Francisco, California
- Term: Fall 2026 (September 14th to December 18th)
- Modality: On-site
- Employment: Full-time
- Compensation: Paid position ($58 to $63 per hour)
