2 months ago
Remote, United StatesMid Level
Base Salary
$150k - $300k/yr
Responsibilities
- Design and build custom RL environments and digital twins that simulate complex enterprise workflows.
- Post-train LLM-based agents on domain-specific tasks using PPO, GRPO, DPO, RLHF, and related methods.
- Build end-to-end pipelines that convert human-labeled traces into RL training data.
- Architect multi-step reasoning agents with tool-calling and closed learning loops.
- Design reward functions, programmatic verifiers, and validation frameworks for pre-deployment testing.
- Translate RL research into scalable production systems and contribute to publications.
- Help shape product direction and make governed RL accessible to enterprise customers.
Requirements
- At least 3 years of hands-on experience with RL environment design, reward engineering, and policy optimization.
- Experience fine-tuning LLMs using RLHF, DPO, PPO, or similar techniques.
- Production software engineering experience beyond research, including scalable pipelines and training infrastructure.
- Experience with LLM-based agents, tool use, and multi-step reasoning.
- Strong Python skills and experience with Gymnasium, RLlib, Stable Baselines, and PyTorch, JAX, or TensorFlow.
- MS or PhD in computer science, machine learning, or a related field, or equivalent experience.
- Preferred: publications at NeurIPS, ICML, ICLR, ACL, or similar venues.
- Preferred: enterprise workflow experience in healthcare, finance, logistics, or compliance.
- Preferred: open-source contributions to CleanRL, TRL, veRL, or agent frameworks.
- Preferred: experience with world models, synthetic data generation, simulation, distributed training, and large-scale RL experimentation.
Benefits
- Hybrid/remote work arrangement in Palo Alto, California or Seattle, Washington.
- Opportunity to shape a new discipline at the intersection of reinforcement learning, simulation, and enterprise AI.
- Opportunity to apply research to real systems across healthcare, finance, and safety.
- Collaboration with NVIDIA, Microsoft, and the global AI community.
Categories
AI ResearchML Engineering
