Senior RL Post-Training Systems Engineer (Equity)

NVIDIA; Washington; On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Comprehensive benefits

Job summary

NVIDIA is seeking a senior engineer to architect and build scalable RL post-training infrastructure across GPUs, CPUs, and LPUs. You will optimize training-inference-rollout loops, contribute to open-source RL frameworks, and partner with cross-functional teams to ensure fault-tolerant, elastic, and fast-restart capabilities for large-scale distributed training environments.

You will collaborate with networking, math library, and compiler teams to prioritize RL workload capabilities and take

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 experimentation on a single GPU to production across thousands of nodes
• Tune RL training-inference-rollout loops on GPUs, CPUs, and LPUs for performance
• Improve the performance and usability of open-source RL frameworks and partner with their teams
• Ensure fault tolerance, elastic scaling, and fast restarts for long-running distributed training jobs
• Collaborate with teams building CPU-driven rollout workloads and advocate for researcher and partner needs with NVIDIA teams

Skills

Python

C/C++

Distributed systems

Communication skills

Education

MS or PhD in CS/CE or related

Tools

PyTorch

Kubernetes

Ray

Job description

NVIDIA is seeking a senior engineer to architect and build scalable RL post-training infrastructure across GPUs, CPUs, and LPUs. You will optimize training-inference-rollout loops, contribute to open-source RL frameworks, and partner with cross-functional teams to ensure fault-tolerant, elastic, and fast-restart capabilities for large-scale distributed training environments.

You will collaborate with networking, math library, and compiler teams to prioritize RL workload capabilities and take