RL Post-Training Systems Architect (Equity Eligible)

NVIDIA Gruppe; Santa Clara (CA); Hybrid; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Job summary

NVIDIA seeks an engineer to architect and scale RL post-training infrastructure from experimentation on a single GPU to production across thousands of nodes. You will optimize training-inference loops on GPUs, CPUs, and LPUs while improving open-source RL frameworks and partnering with cross-team groups.

The role requires expertise in distributed systems, Python and C/C++, and experience delivering production-grade runtime frameworks at large AI labs or hyperscalers. Hybrid work is supported.

Qualifications

• MS or 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 a single GPU to thousands of nodes
• Tune RL training-inference-rollout loops for performance on GPUs, CPUs, and LPUs
• Improve performance and usability of open-source RL frameworks
• Collaborate with NVIDIA teams (networking, math libraries, compilers) and hardware teams to enable next-gen post-training workloads

Skills

Python

C/C++

Distributed systems

Communication

Education

MS or PhD in CS/CE

Tools

Kubernetes

PyTorch

Ray

NCCL

Job description

NVIDIA seeks an engineer to architect and scale RL post-training infrastructure from experimentation on a single GPU to production across thousands of nodes. You will optimize training-inference loops on GPUs, CPUs, and LPUs while improving open-source RL frameworks and partnering with cross-team groups.

The role requires expertise in distributed systems, Python and C/C++, and experience delivering production-grade runtime frameworks at large AI labs or hyperscalers. Hybrid work is supported.