Senior RL Post-Training Frameworks Architect

NVIDIA; Santa Clara (CA); Hybrid; 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. You will architect and build RL post-training infrastructure that scales from single-GPU experiments to production across thousands of nodes, tuning training-inference-rollout loops for performance on GPUs, CPUs, and LPUs.

The role spans fault tolerance, elastic scaling, and fast restarts, and you will partner with teams building

Qualifications

• MS/PhD in Computer Science, Computer Engineering, or a related field (or equivalent experience)
• 5+ years of professional experience in distributed systems, high-performance computing, deep learning infrastructure, or ML systems engineering
• Strong proficiency in Python and C/C++
• Demonstrated experience building or contributing to large-scale distributed systems or runtime frameworks in production at a frontier AI lab, hyperscaler, or major technology company
• Strong verbal and written communication skills and the ability to collaborate across organizational and geographic boundaries

Responsibilities

• Architect and build RL post-training infrastructure that scales from single-GPU experimentation to production across thousands of nodes
• Tune RL training-inference-rollout loops on GPUs, CPUs, and LPUs for performance
• Contribute to and improve the performance and usability of open-source RL frameworks
• Partner with teams who own frameworks; work on fault tolerance, elastic scaling, and fast restarts for long-running distributed jobs
• Collaborate with hardware, networking, math library, and compiler teams to meet RL workload needs

Skills

Python

C/C++

Distributed systems

Deep learning infra

Communication

Education

MS/PhD in Computer Science 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. You will architect and build RL post-training infrastructure that scales from single-GPU experiments to production across thousands of nodes, tuning training-inference-rollout loops for performance on GPUs, CPUs, and LPUs.

The role spans fault tolerance, elastic scaling, and fast restarts, and you will partner with teams building