RL Post-Training Infrastructure Architect

NVIDIA; Washington (District of Columbia); On-site; USD 184,000 - 357,000; Full time

Job summary

NVIDIA is building an RL Frameworks engineering team to develop the open-source tools and infrastructure that AI researchers and post-training teams depend on. The team spans the full software stack, from collaborating closely with researchers to improving runtimes like Ray and Monarch.

You will architect and build RL post-training infrastructure that scales from a single GPU to production across thousands of nodes, tuning loops for performance and reliability across GPUs, CPUs, and LPUs.

Qualifications

• MS or PhD in CS/CE or related field or equivalent experience.
• 5+ years of distributed systems or ML infrastructure experience.
• Strong Python and C/C++ proficiency.
• Excellent verbal and written communication across teams.

Responsibilities

• Architect and build RL post-training infrastructure at scale.
• Tune RL training-inference-rollout loops across GPUs/CPUs/LPUs.
• Improve open-source RL frameworks and distributed runtimes.

Skills

Python

C/C++

Distributed systems

Communication skills

Education

MS or PhD in Computer Science / Computer Engineering or related field

Tools

VeRL

Miles

TorchTitan

Ray

Monarch

Job description

NVIDIA is building an RL Frameworks engineering team to develop the open-source tools and infrastructure that AI researchers and post-training teams depend on. The team spans the full software stack, from collaborating closely with researchers to improving runtimes like Ray and Monarch.

You will architect and build RL post-training infrastructure that scales from a single GPU to production across thousands of nodes, tuning loops for performance and reliability across GPUs, CPUs, and LPUs.