Member of Technical Staff - Research, Post-Training
New York • FullTime
Posted 2mo ago
Remote Work Policy
On-site
Employment Type
FullTime
Categories
AI Research Engineer
About the job
We are building a platform that covers the entire lifecycle of Large Language Models (LLMs), from training to deployment and production observation. Our existing infrastructure supports multi-node training, elastic inference, sandboxes, and distributed volumes, with full control over the underlying systems. We are seeking individuals with deep research expertise in post-training techniques to complement our existing systems and product development efforts. This role is ideal for candidates passionate about improving current methods and creating novel techniques for large-scale model training, optimization, and inference. You will focus on extending models to handle long-context and long-horizon tasks, and enhancing inference-time efficiency, reliability, and robustness for critical real-world applications.
Responsibilities
- Improve existing methods and develop new techniques for large-scale model training, optimization, and inference.
- Extend models to handle long-context and long-horizon tasks.
- Improve inference-time efficiency, reliability, and robustness in high-stakes real-world deployments.
Requirements
- PhD in computer science, machine learning, or a related field (or Master's degree with significant research/industry experience).
- 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.
- Experience developing, training, optimizing, or deploying state-of-the-art large-scale models.
- First-author publications at leading AI/ML venues (e.g., NeurIPS, ICML, ICLR, CoRL, CVPR, UAI, JMLR, TMLR).
- Mission-driven mindset and desire to translate research into product impact.
- Collaborative spirit and ability to work effectively across research and engineering teams.