RL AI Research Scientist

Pokee AI; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Pokee AI is seeking an RL Research Scientist to join a team building the reinforcement learning foundation of an AI agent platform. The role focuses on long-horizon planning, reward shaping, and context selection to enable reliable operation at scale.

Remote role with a preference for candidates based in the US or Singapore. You will design and implement novel RL algorithms, develop reward models, and run large-scale experiments, translating insights into production-ready components while

Qualifications

• PhD or equivalent research experience in RL, ML, or a closely related field.
• Strong publication record at top venues (NeurIPS, ICML, ICLR, AAAI).
• Deep expertise in RL fundamentals (policy gradients, value methods, model-based RL, multi-agent RL, RLHF/RLAIF).
• Proficiency in Python and at least one DL framework; PyTorch preferred.
• Proven ability to translate research from prototype to production.

Responsibilities

• Design and implement novel RL algorithms for training agents on complex, multi-step enterprise workflows.
• Develop reward modeling, context selection, and policy optimization techniques for long horizons.
• Run large-scale experiments, analyze results, and translate research into production-ready components.
• Collaborate with infrastructure engineers to scale research prototypes on cloud and on-device hardware.
• Track advances in RL, LLM fine-tuning, and agent architectures; propose new directions.
• Contribute to IP through publications, patents, and open-source work.

Skills

Reinforcement Learning

Machine Learning

Python

PyTorch

Research to Production

Education

PhD or equivalent RL/ML research

Job description

Role overview

An RL AI Research Scientist role on a research team building the reinforcement learning foundation of an AI agent platform. The work targets real-world enterprise tasks, with emphasis on long-horizon planning, reward shaping, and context selection that allow agents to operate reliably at scale. The position is remote, with a preference for candidates based in the US or Singapore.

Responsibilities

• Design and implement novel reinforcement learning algorithms for training agents on complex, multi-step enterprise workflows.
• Develop reward modeling, context selection, and policy optimization techniques that improve agent accuracy over extended horizons.
• Run large-scale experiments, analyze results rigorously, and translate research into production-ready components.
• Collaborate with infrastructure engineers to ensure research prototypes scale efficiently on cloud and on-device hardware.
• Track advances in RL, LLM fine-tuning, and agent architectures, and propose new research directions.
• Contribute to intellectual property through publications, patents, and open-source work.

Requirements

• PhD or equivalent research experience in Reinforcement Learning, Machine Learning, or a closely related field.
• Strong publication record at top venues such as NeurIPS, ICML, ICLR, or AAAI.
• Deep expertise in RL fundamentals, including 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, with a strong preference for PyTorch.
• Demonstrated ability to move research from prototype to production.

Nice to have

• Experience training and fine-tuning large language models.
• Background in on-device or edge inference optimization, including quantization, distillation, or mixture-of-experts architectures.
• Familiarity with enterprise software deployment, compliance, or regulated industries.
• Open-source contributions in RL or LLM ecosystems.
• Experience with distributed training at scale using frameworks such as FSDP, DeepSpeed, or Megatron.