Agent RL Infra Engineer

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

• 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 from the crowd:

• 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

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD.