Member of Technical Staff - Research, Post-Training

The role

This person will improve methods and develop new techniques for large-scale model training, optimization, and inference. They will work on extending models to long-context and long-horizon tasks and improving inference-time efficiency, reliability, and robustness.

What you will do

• 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 in high-stakes real-world deployments

Required qualifications

• PhD in computer science, machine learning, or a related field
• Demonstrated record of research accomplishments in reinforcement learning, machine learning, foundation models, or related fields
• Experience with large-scale training and inference infrastructure, including distributed systems and multi-node GPU clusters
• Developed, trained, optimized, or deployed state-of-the-art large-scale models
• First-author publications at leading venues such as NeurIPS, ICML, ICLR, CoRL, CVPR, UAI, JMLR, or TMLR
• Mission-driven mindset
• Strong desire to translate research advances into meaningful product impact
• Collaborative spirit
• Ability to work effectively across research and engineering teams

Nice to have

• Master's degree and significant research or industry experience

About the company

Modal is building a new infrastructure layer for AI. The company has passed $1B in annualized run rate and includes creators of popular open-source projects and experienced engineering and product leaders.