Member of Technical Staff - Research, Post-Training
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At a glance
Job overview
Modal Labs is hiring a Member of Technical Staff - Research, Post-Training. Modal Labs is building an AI infrastructure platform covering the entire LLM lifecycle, from training to deployment and production monitoring, and seeks a researcher to lead post‑training research, collaborate with customers and engineers, and shape the research agenda.
Key focus areas include Own end-to-end post‑training research bets including async and agentic RL, on‑policy distillation, long‑context RL, and small routing models, Work directly with customers alongside forward deployed engineers to train models and integrate findings into research, and Carry and expand collaborations with outside research labs such as ZLab on speculator designs.
Successful candidates bring Research-Leaning Background In Post-Training LLMs, Work You Can Point To, and Record Of Shipping Research. Important skills include Post-Training LLMs, Product Sense, Shipping Research, and Research Bet Ownership. Preferred (not required): Async And Agentic RL, On-Policy Distillation, Long-Context RL, and Small Routing Models.
Skills & qualifications
Skills
Qualifications
Full job description
About Us: AI needs a new infrastructure layer. 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. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
The Role: We're building a platform that covers the whole life of an LLM: training it, deploying it, and observing it in production. 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.
Preferred Qualifications:
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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.
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A demonstrated record of research accomplishments in reinforcement learning, machine learning, foundation models, or related fields.
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Experience with large-scale training and inference infrastructure, including distributed systems and multi-node GPU clusters.
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Experience developing, training, optimizing, or deploying state-of-the-art large-scale models.
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First-author publications at leading venues such as NeurIPS, ICML, ICLR, CoRL, CVPR, UAI, JMLR, or TMLR.
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A mission-driven mindset and a strong desire to translate research advances into meaningful product impact.
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A collaborative spirit and the ability to work effectively across research and engineering teams.
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