Modal
Member of Technical Staff - Research, Post-Training
$150K–$350K
New York +1 · Staff · Full-time · Posted Jul 5, 2026
- Location
- New York
San Francisco - Experience
- No specific number of years stated; candidates with a master’s degree and significant research or industry experience will also be considered.
- Education
- PhD in computer science, machine learning, or a related field; candidates with a master’s degree and significant research or industry experience will also be considered.
About the job
Conduct post-training research and develop methods to improve large-scale model training, optimization, inference, and deployment reliability.
What you'll do
- Improve existing methods and develop 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 deployments.
What they ask for
- Demonstrated 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 venues such as NeurIPS, ICML, ICLR, CoRL, CVPR, UAI, JMLR, or TMLR.
Pay
$150K – $350K (base salary)
Tools
large language models, multimodal models, distributed systems, multi-node GPU clusters
AI focus
Research and post-training for foundation models, including reinforcement learning, large-scale training and inference, long-context and long-horizon tasks, and inference-time efficiency and robustness.
Model-written summary · the original posting is authoritative