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Member of Technical Staff - Research, Post-Training

Modal· Engineering
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New York, San Francisco$150k - $350kFull-timePosted 3mo ago

About the role

Research Scientist role focusing on reinforcement learning, machine learning, and foundation models to advance post-training techniques for large-scale model training, optimization, and inference, with emphasis on long-context tasks, efficiency, and robust production deployment.

Responsibilities

  • Research scientists with a strong track record in reinforcement learning, machine learning, and foundation models to join the research team.
  • Develop methods 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.

Qualifications

  • A PhD in computer science, machine learning, or a related field; or a master's with significant research or industry experience.
  • 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 (e.g., NeurIPS, ICML, ICLR, CoRL, CVPR, UAI, JMLR, TMLR).
  • Mission-driven mindset with a desire to translate research into product impact.
  • Collaborative ability to work across research and engineering teams.

Skills

Required:Reinforcement learningFoundation modelsLarge-scale training and inference
Last updated Oct 21, 2026. Interested? Apply on the company site.
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High-performance AI infrastructure

Bring your own code, and run CPU, GPU, and data-intensive compute at scale. The serverless platform for AI and data teams.

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