RL AI Research Scientist

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RL AI Research Scientist
Preferred
Research Remote (US/Singapore Preferred) Full-time
Design, implement, and scale novel reinforcement learning algorithms that form the core of Pokee's AI agent platform.
About The Role
As an RL Research Scientist, you will design, implement, and scale novel reinforcement learning algorithms that form the core of Pokee’s AI agent platform. You’ll work at the frontier of RL applied to real-world enterprise tasks—developing methods for context selection, long-horizon planning, and reward shaping that enable agents to operate reliably at scale.
What You’ll Do
Design and implement novel RL algorithms for training AI agents on complex, multi-step enterprise workflows
Develop and refine reward modeling, context selection, and policy optimization techniques that improve agent accuracy over extended task horizons
Run large-scale experiments, analyze results rigorously, and translate research findings into production-ready components
Collaborate closely with infrastructure engineers to ensure research prototypes scale efficiently on both cloud and on-device hardware
Contribute to the company’s intellectual property through publications, patents, and open-source contributions
Stay current with the latest advances in RL, LLM fine-tuning, and AI agent architectures, and propose new research directions
What We’re Looking For
Required
PhD (or equivalent research experience) in Reinforcement Learning, Machine Learning, or a closely related field
Strong publication record at top venues (NeurIPS, ICML, ICLR, AAAI, or equivalent)
Deep expertise in RL fundamentals: policy gradient methods, value-based methods, model-based RL, multi-agent RL, or RLHF/RLAIF
Proficiency in Python and at least one deep learning framework (PyTorch strongly preferred)
Experience training and fine-tuning large language models is a significant plus
Demonstrated ability to take research from prototype to production
Bonus Points
Experience with on-device or edge inference optimization (quantization, distillation, MoE architectures)
Familiarity with enterprise software deployment, compliance, or regulated industries
Track record of open-source contributions in RL or LLM ecosystems
Experience with distributed training at scale (FSDP, DeepSpeed, Megatron)
Who You Are
You want to join a small, elite team solving one of the hardest problems in AI—building agents that actually work in the real world. You’ll have direct impact on the product, access to cutting-edge research, and the opportunity to shape the future of enterprise AI from the ground up.
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