Design and build custom RL environments (digital twins) simulating enterprise workflows: document processing, compliance, onboarding, support automation
Post-train LLM-based agents on domain-specific tasks using PPO, GRPO, DPO, and RLHF
Build end-to-end pipelines converting human-labeled traces into RL training data
Architect multi-step reasoning agents with tool-calling and closed learning loops
Design reward functions, verifiers, and validation frameworks for pre-deployment testing
Translate cutting-edge RL research into production systems; contribute to publications

Deep RL expertise: 3+ years hands-on experience with environment design, reward engineering, policy optimization
LLM post-training: Experience fine-tuning LLMs using RLHF, DPO, PPO, or similar
Production skills: Software engineering beyond research with scalable pipelines and training infrastructure
Agentic AI: Experience with LLM-based agents, tool use, multi-step reasoning
Technical stack: Strong Python; Gymnasium, RLlib, Stable Baselines; PyTorch/JAX/TensorFlow
Education: MS/PhD in CS, ML, or related field (or equivalent experience)

Lead the frontier: Shape a new discipline at the intersection of RL, simulation, and enterprise AI
Ship your science: See your research power real systems across healthcare, finance, and safety
Collaborate with leaders: Work alongside NVIDIA, Microsoft, and the global AI community
Build what matters: Create governed, compliant AI systems enterprises can trust

Applied Reinforcement Learning Engineer

Key skills