ML Research Intern
Modal Labs, Inc. operates Modal, a serverless cloud and AI infrastructure platform for developers running inference, training, batch processing, and isolated sandboxes.
Funding history
About Modal
Modal provides code-first, elastic CPU/GPU compute infrastructure for AI workloads, including model inference, fine-tuning and training, large-scale batch jobs, and secure ephemeral execution environments.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will research and develop methods for large-scale model training, optimization, and inference. You will extend models to long-context and long-horizon tasks, and improve inference-time efficiency, reliability, and robustness for high-stakes real-world deployments. You will translate research ideas into working implementations and evaluate large-scale models or machine-learning systems.
Requirements
- Currently pursuing a PhD in computer science, machine learning, or a related field
- Research record in reinforcement learning, machine learning, foundation models, or related areas
- Experience developing and evaluating large-scale models or machine-learning systems
- Familiarity with distributed training, large-scale inference, or multi-GPU environments
- Strong programming and engineering skills
Responsibilities
- Improve existing methods and develop new techniques for large-scale model training, optimization, and inference
- Extend models to long-context and long-horizon tasks
- Improve inference-time efficiency, reliability, and robustness
- Translate research ideas into working implementations
- Develop and evaluate large-scale models or machine-learning systems