# Agent RL Infra Engineer **Company**: NVIDIA **Location**: Santa Clara **Work arrangement**: onsite **Experience**: senior **Job type**: full-time **Category**: Engineering **Industry**: Technology **Apply**: https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Agent-RL-Infra-Engineer_JR2015309?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_1a1bcc91-655 ## Description We're hiring an engineer to help us bring reinforcement learning to every agent team at NVIDIA. This is a rare chance to shape how autonomous, self-improving agents learn and evolve across the enterprise. The role sits at the intersection of ML research and production engineering. What if every agent developer could add self-improvement loops to their workflows without needing deep RL expertise? That's the challenge here: evaluate emerging approaches, adapt them into enterprise-ready blueprints, and make them available inside sandboxed execution environments with the security and governance the enterprise demands. ### Responsibilities The work splits between creating enterprise-ready RL capabilities and partnering with agent teams to put them into practice. #### Building RL Cookbooks and Environments - Evaluate and adapt democratized RL approaches into reusable cookbooks and blueprints so agent developers can integrate self-improvement loops (GRPO, DPO, PPO, RLAIF) on their own - Design verifiable reward environments building on NeMo Gym, extending to domain-specific environments for internal use cases - Operationalize NVIDIA and third-party training backends as production services inside Sandbox - Integrate with NeMo Microservices (Curator, Customizer, Evaluator, Guardrails) to enable end-to-end data flywheel workflows for RL #### Infrastructure, Reliability, and Collaboration - Lead data curation and active learning strategies to continuously improve training data quality - Design RL training loops for agent self-improvement: reward modeling, policy optimization, safety constraints - Integrate with AI Factory GPU infrastructure for throughput, data locality, and multi-node training - Build observability for training runs and ensure workloads meet security and governance requirements - Collaborate with platform, security, agent infrastructure, and internal customer teams on safe deployment of training outputs ### Requirements - MS in CS, ML, or related field (or equivalent experience) - 10+ years of experience - Experience operationalizing fine-tuning methods (LoRA, SFT) and especially RL techniques (DPO, GRPO, PPO, RLAIF) into reusable cookbooks and self-service workflows - Familiarity with distributed training frameworks (e.g., Megatron, NeMo, DeepSpeed, FSDP, HF Accelerate) and ML ops skills covering pipeline automation, job orchestration, and GPU cluster management are important here - Proficiency in Python, Go, Rust, or similar - Background in CS, ML, or related field through formal education or equivalent experience ### Ways to Stand Out - Building RL environments or training recipes that other teams consumed as self-service capabilities - Familiarity with NVIDIA infrastructure (DGX, AI Factory, NVLink/InfiniBand), NeMo Microservices, or the evolving RL-for-agents ecosystem (rLLM, Agent Lightning, HUD, OpenRLHF, SkyRL) - Experience with data curation, active learning, continuous learning loops, or data flywheel architectures also valued You will also be eligible for equity and benefits. ## Skills ### Required - Python - Go - Rust - Megatron - NeMo - DeepSpeed - FSDP - HF Accelerate - LoRA - SFT - DPO - GRPO - PPO - RLAIF ### Nice to have - NVIDIA infrastructure - NeMo Microservices - RL-for-agents ecosystem - data curation - active learning - continuous learning loops - data flywheel architectures --- Source: [Apply at nvidia.wd5.myworkdayjobs.com](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Agent-RL-Infra-Engineer_JR2015309?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)