RL AI Research Scientist
Pokee AI · United States
- Hybrid
- Full-time
- $150,000 / year
- United States
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Job highlights
- Design and implement novel RL algorithms for AI agents.
- Develop advanced reward modeling and policy optimization.
- Conduct large-scale experiments and production deployment.
- Collaborate on scaling research prototypes.
- Contribute to IP via publications and patents.
About the role
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
- 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.
Key skills/competency
- Reinforcement Learning
- Machine Learning
- Python
- PyTorch
- Large Language Models (LLMs)
- AI Agents
- Algorithm Design
- Scalability
- Research
- Production Deployment
Skills & topics
- Reinforcement Learning
- AI Research
- Machine Learning Scientist
- Python
- PyTorch
- LLM
- AI Agents
- Research Scientist
- Algorithm Development
- Enterprise AI
How to get hired
- Tailor your resume: Highlight your PhD/research experience and publications in RL/ML to match the 'RL AI Research Scientist' role.
- Showcase expertise: Emphasize your deep knowledge in RL fundamentals and proficiency in Python/PyTorch.
- Demonstrate impact: Provide examples of taking research from prototype to production, especially with LLMs.
- Prepare for interviews: Be ready to discuss your research, problem-solving approach, and contributions to top AI venues.
Technical preparation
Master RL fundamentals: policy gradients, value methods.,Practice Python and PyTorch for ML models.,Implement algorithms for LLM fine-tuning.,Simulate agent behavior in complex workflows.
Behavioral questions
Describe a challenging research project you led.,How do you translate research into production code?,How do you stay updated on AI advancements?,Explain your experience with large-scale experiments.
Frequently asked questions
- What are the key responsibilities for an RL AI Research Scientist at Pokee AI?
- As an RL AI Research Scientist at Pokee AI, you will design, implement, and scale novel reinforcement learning algorithms for Pokee's AI agent platform. This includes developing methods for context selection, long-horizon planning, and reward shaping to ensure agents operate reliably at scale in real-world enterprise tasks. You will also run large-scale experiments, collaborate with engineers, and contribute to intellectual property through publications and patents.
- What qualifications are required for the RL AI Research Scientist position at Pokee AI?
- Pokee AI requires a PhD (or equivalent research experience) in Reinforcement Learning, Machine Learning, or a related field, with a strong publication record at top venues like NeurIPS, ICML, or ICLR. Essential skills include deep expertise in RL fundamentals (policy gradient, value-based, model-based RL, RLHF/RLAIF), proficiency in Python, and experience with a deep learning framework like PyTorch. The ability to transition research from prototype to production is also crucial.
- Is experience with Large Language Models (LLMs) necessary for the RL AI Research Scientist role at Pokee AI?
- While not strictly required, experience training and fine-tuning large language models is considered a significant plus for the RL AI Research Scientist position at Pokee AI. This experience can greatly enhance your application, especially as LLM fine-tuning is a key area of research Pokee AI is involved in.
- What are the preferred locations for the RL AI Research Scientist position at Pokee AI?
- Pokee AI prefers candidates located in the United States or Singapore for this remote position. While remote work is offered, these locations are preferred for logistical or strategic reasons.
- What kind of research challenges can I expect as an RL AI Research Scientist at Pokee AI?
- You can expect to work on cutting-edge research challenges at the frontier of applying RL to real-world enterprise tasks. This includes developing sophisticated methods for context selection, long-horizon planning, and reward shaping to enable AI agents to operate reliably and effectively on complex, multi-step workflows at scale.
- How does Pokee AI support professional development and intellectual contributions for its researchers?
- Pokee AI encourages its researchers to contribute to the company’s intellectual property through publications in top venues, patent applications, and open-source contributions. You will also be encouraged to stay current with the latest advances in RL and LLMs, and to propose new research directions, fostering a dynamic research environment.