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About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
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The Senior Software Engineer, RL Post-Training Frameworks joins the RL Frameworks engineering team to develop open-source tools and infrastructure for reinforcement learning post-training. You will architect and build scalable infrastructure that manages training-inference-rollout loops across thousands of nodes, ensuring fault tolerance and elastic scaling for long-running distributed jobs. Key responsibilities include tuning performance on GPUs, CPUs, and LPUs, and contributing to frameworks like VeRL, Miles, and TorchTitan while improving runtimes such as Ray and Monarch. You will utilize Python, C/C++, and PyTorch internals to solve complex distributed systems challenges, including heterogeneous placement and environment execution. The role addresses the technical difficulty of coordinating actor, critic, and reward models to enable models to reason through hard problems and act as autonomous agents.
What does a Software Engineer earn in California?
Median $214500 from 1193 postings across 76 companies.
Architect and build RL post-training infrastructure that scales from single GPUs to thousands of nodes.
Tune RL training-inference-rollout loops for performance across GPUs, CPUs, and LPUs.
Improve the performance and usability of open-source RL frameworks like VeRL, Miles, and TorchTitan.
Implement fault tolerance, elastic scaling, and fast restarts for long-running distributed training jobs.
Develop systems for CPU-driven rollout workloads including tool-use, code execution, and agentic environments.
Advocate for RL workload requirements to networking, math library, and compiler teams.
Collaborate with hardware teams to leverage next-generation capabilities for post-training workloads.
What we're looking for
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.
Depth in one or more areas including RL for LLM post-training, PyTorch internals, Kubernetes runtime internals, or end-to-end distributed systems design.
Experience in networking, advanced multi-dimensional parallelisms, memory optimizations, high-performance inference engines, or actor-based distributed programming (preferred).
Open-source contributions, advanced Kubernetes work, or direct experience operating frontier-scale training (preferred).
PyTorch
Python
C++
Distributed Training
NCCL
DDP
FSDP
Kubernetes
Slurm
RoCE
Lustre
Deep Learning
Performance Profiling
Reinforcement Learning
Fault Resilience