Senior Software Engineer, RL Post-Training Frameworks

🔥 34 minutes ago

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🤖 Artificial Intelligence

🎮 Gaming

Artificial Intelligence • Gaming • Automotive

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Architect and build RL post-training infrastructure that scales efficiently from experimentation on a single GPU to production across thousands of nodes. • Tuning RL training-inference-rollout loops on GPUs, CPUs, and LPUs for performance where it matters. • Contributing to and improving the performance and usability of open-source RL frameworks. • Partnering with teams building CPU-driven rollout workloads, including tool-use, code execution, and agentic environments. • Advocating for researcher and partner needs with NVIDIA's networking, math library, and compiler teams.

🎯 Requirements

• 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 of the following technical areas: Reinforcement learning for LLM post-training (RLHF, PPO, GRPO, DPO, reward modeling), including how algorithms map to distributed execution and the systems challenges they create (heterogeneous placement, rollouts, environment execution, resharding between training and generation) • PyTorch internals, including distributed training primitives (FSDP, tensor parallelism, pipeline parallelism) and their composition • Kubernetes runtime internals (container lifecycle, pod scheduling, resource quotas, GPU allocation) • End-to-end distributed systems design (service boundaries, data flows, consistency models, failure modes, recovery approaches)

🏖️ Benefits

• Highly competitive salaries • Comprehensive benefits package