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. We believe the best training and self-evolving agent platforms come from people with diverse backgrounds and want this person to help us build ours.

What you'll be doing:

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 Ne Mo Gym, extending to domain-specific environments for internal use cases

• Operationalize NVIDIA and third-party training backends as production services inside Sandbox

• Integrate with Ne Mo 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

What we need to see:

• 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, Ne Mo, Deep Speed, 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/Infini Band), Ne Mo 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.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until April 2, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.