ML Research Intern: Foundation Models & RL

Mixpeek; San Francisco (CA); On-site; USD 60,000 - 90,000; Full time

Benefits offered by this job

Mentorship by senior researchers

Research publication opportunities

PhD internship stipend

Job summary

Modal is seeking PhD research interns with strong experience in reinforcement learning, machine learning, and foundation models to join our research team in San Francisco. The internship focuses on improving methods for large-scale model training, optimization, and inference, extending models to long-context tasks and enhancing inference efficiency in real-world deployments.

You will collaborate with researchers and engineers, publish results at leading venues, and translate ideas into working

Qualifications

• PhD candidate in CS/ML or related field.
• Track record in RL/ML research.
• Experience with large-scale ML systems.
• Familiarity with distributed training / multi-GPU environments.
• Publications in leading venues (NeurIPS/ICML/ICLR/etc).
• Strong programming and engineering skills with research ideas to working implementations.

Responsibilities

• Advance large-scale model training, optimization, and inference methods.
• Extend models to long-context and long-horizon tasks.
• Improve inference-time efficiency, reliability, and robustness in real-world deployments.
• Collaborate with research and engineering teams.

Skills

Reinforcement Learning

Machine Learning

Foundation Models

Large Language Models

Multimodal Models

Distributed Training

Multi-GPU Environments

Publications

Programming Skills

Education

PhD candidate in CS/ML or related field

Tools

Python

PyTorch

TensorFlow

Job description

Modal is seeking PhD research interns with strong experience in reinforcement learning, machine learning, and foundation models to join our research team in San Francisco. The internship focuses on improving methods for large-scale model training, optimization, and inference, extending models to long-context tasks and enhancing inference efficiency in real-world deployments.

You will collaborate with researchers and engineers, publish results at leading venues, and translate ideas into working