Member of Technical Staff - Research, Post-Training

Overview

We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade.

What you'll do

• We already run multi-node training, elastic inference, sandboxes, and distributed volumes, and we control the infrastructure underneath.
• We’re looking for research depth in post-training to sit alongside our systems and product work.
• WHAT YOU'LL DO: We are looking for research scientists with a strong track record in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team.
• This role is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments.

Requirements

• A 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.
• A 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 venues such as NeurIPS, ICML, ICLR, CoRL, CVPR, UAI, JMLR, or TMLR.
• A mission-driven mindset and a strong desire to translate research advances into meaningful product impact.
• A collaborative spirit and the ability to work effectively across research and engineering teams.

Skills